This project is mainly based on image processing technique. In this work MATLAB have been used through every procedure made. Image processing techniques are widely use in bio-medical sector. The objective of our work is noise removal operation, thresholding, gray scale imaging, histogram equalization, texture segmentation, and morphological operation. Detection of lung cancer from computed tomography (CT) images is done by using MATLAB software. By using these methods the work has been done on CT images and the final tumor area has been shown with pixel values. Bindiya Patel | Dr. Pankaj Kumar Mishra | Prof. Amit Kolhe"Lung Cancer Detection on CT Images by using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11674.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/11674/lung-cancer-detection-on-ct-images-by-using-image-processing/bindiya-patel
Lung Cancer Detection using Image Processing TechniquesIRJET Journal
This document presents a technique for detecting lung cancer in x-ray images using image processing. It involves enhancing images using Gabor filtering, segmenting images using marker-controlled watershed segmentation, and extracting features using binarization and masking. The key steps are collecting lung x-ray images, enhancing quality using Gabor filtering, segmenting regions of interest using watershed segmentation, extracting pixel counts and mask features, and classifying images as normal or abnormal based on these features. The goal is to enable early detection of lung cancer through automated analysis of medical images.
PPT on BRAIN TUMOR detection in MRI images based on IMAGE SEGMENTATION khanam22
The document presents three methods for tumor detection in MRI images: 1) K-means clustering with watershed algorithm, 2) Optimized K-means using genetic algorithm, and 3) Optimized C-means using genetic algorithm. It evaluates each method, finding that C-means clustering with genetic algorithm most accurately detects tumors by assigning data points to multiple clusters and finding the optimal solution in less time. The proposed approach successfully detects tumors with high accuracy, identifies the tumor area and internal structure, and provides a colorized output image.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Image processing in lung cancer screening and treatmentWookjin Choi
The document discusses image processing techniques for lung cancer screening and treatment. It covers topics like lung segmentation, nodule detection, computer-aided diagnosis, image-guided radiotherapy, and quantitative assessment of tumor response. Lung segmentation is used to isolate the lungs from other organs in CT images. Nodule detection algorithms then aim to find potential cancerous nodules. Computer-aided diagnosis systems analyze extracted features of nodules to determine if they are malignant or benign. Image-guided radiotherapy utilizes 4D CT and gating to account for tumor motion during treatment. Quantitative metrics like standardized uptake value are used to assess tumor response in PET imaging.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSINGkajikho9
The document describes a lung cancer detection system using digital image processing. It discusses preprocessing techniques like Gabor filtering and FFT that are applied to enhance images. Segmentation methods like thresholding and marker-controlled watershed are used to segment lung regions. Features are extracted using binarization and masking approaches to detect cancer presence. The system analyzes images and indicates whether cases are normal or abnormal by detecting white masses inside lung regions, helping diagnose lung cancer at early stages.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Lung Cancer Detection Using Convolutional Neural NetworkIRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to classify lung cancer in CT scans. The CNN model is trained on a dataset of 1018 patient CT scans containing annotations of lung nodules as benign or malignant. The CNN architecture includes convolution layers to extract features, max pooling layers to reduce computations, dropout layers to prevent overfitting, and fully connected layers to classify scans. The model achieves a 65% accuracy on the training set at detecting cancer in new CT scans. The CNN is integrated into a web application to allow doctors to efficiently analyze scans for lung cancer.
Lung Cancer Detection using Image Processing TechniquesIRJET Journal
This document presents a technique for detecting lung cancer in x-ray images using image processing. It involves enhancing images using Gabor filtering, segmenting images using marker-controlled watershed segmentation, and extracting features using binarization and masking. The key steps are collecting lung x-ray images, enhancing quality using Gabor filtering, segmenting regions of interest using watershed segmentation, extracting pixel counts and mask features, and classifying images as normal or abnormal based on these features. The goal is to enable early detection of lung cancer through automated analysis of medical images.
PPT on BRAIN TUMOR detection in MRI images based on IMAGE SEGMENTATION khanam22
The document presents three methods for tumor detection in MRI images: 1) K-means clustering with watershed algorithm, 2) Optimized K-means using genetic algorithm, and 3) Optimized C-means using genetic algorithm. It evaluates each method, finding that C-means clustering with genetic algorithm most accurately detects tumors by assigning data points to multiple clusters and finding the optimal solution in less time. The proposed approach successfully detects tumors with high accuracy, identifies the tumor area and internal structure, and provides a colorized output image.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Image processing in lung cancer screening and treatmentWookjin Choi
The document discusses image processing techniques for lung cancer screening and treatment. It covers topics like lung segmentation, nodule detection, computer-aided diagnosis, image-guided radiotherapy, and quantitative assessment of tumor response. Lung segmentation is used to isolate the lungs from other organs in CT images. Nodule detection algorithms then aim to find potential cancerous nodules. Computer-aided diagnosis systems analyze extracted features of nodules to determine if they are malignant or benign. Image-guided radiotherapy utilizes 4D CT and gating to account for tumor motion during treatment. Quantitative metrics like standardized uptake value are used to assess tumor response in PET imaging.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSINGkajikho9
The document describes a lung cancer detection system using digital image processing. It discusses preprocessing techniques like Gabor filtering and FFT that are applied to enhance images. Segmentation methods like thresholding and marker-controlled watershed are used to segment lung regions. Features are extracted using binarization and masking approaches to detect cancer presence. The system analyzes images and indicates whether cases are normal or abnormal by detecting white masses inside lung regions, helping diagnose lung cancer at early stages.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Lung Cancer Detection Using Convolutional Neural NetworkIRJET Journal
This document describes a study that uses a convolutional neural network (CNN) to classify lung cancer in CT scans. The CNN model is trained on a dataset of 1018 patient CT scans containing annotations of lung nodules as benign or malignant. The CNN architecture includes convolution layers to extract features, max pooling layers to reduce computations, dropout layers to prevent overfitting, and fully connected layers to classify scans. The model achieves a 65% accuracy on the training set at detecting cancer in new CT scans. The CNN is integrated into a web application to allow doctors to efficiently analyze scans for lung cancer.
An Introduction to Image Processing and Artificial IntelligenceWasif Altaf
This document provides an introduction to image processing and artificial intelligence. It defines what an image is from different perspectives including in literature, general terms, and in computer science as an exact replica of a storage device. It describes image processing as analyzing and manipulating images with three main steps: importing an image, manipulating or analyzing it, and outputting the result. It also discusses what noise is in images, methods to remove noise, color enhancement techniques, sharpening images to increase contrast, and segmentation and edge detection.
This document discusses various types of medical imaging technologies. It describes radiologic/x-ray technology, ultrasound technology, CT scans, MRI scans, and nuclear imaging including PET and SPECT. The goal of medical imaging is to non-invasively examine the inside of the body to diagnose health problems and guide treatment. Each technology has advantages for certain applications based on the type of information and depth of imaging it provides. Together these modalities provide physicians a variety of tools to accurately diagnose and monitor patient health issues.
Breast cancer diagnosis machine learning pptAnkitGupta1476
This document summarizes a presentation on using machine learning to diagnose breast cancer. It introduces machine learning and explains that it uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. It then provides an overview of breast cancer, risk factors, and statistics. It states that machine learning will be used to analyze breast cancer biopsy data to make diagnoses. The document outlines the steps of collecting and exploring the biopsy data, preparing training and test datasets, training a k-nearest neighbors model on the data, and calculating the model's accuracy on the test data using a confusion matrix.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
Brain Tumor Detection Using Deep Neural Network.pptxAbdulRehman417114
The document summarizes a research paper that proposed a new method for detecting brain tumors using MRI images. The method combines a clustering algorithm for feature extraction with a convolutional neural network (CNN). When applied to a dataset of MRI images, the CNN alone achieved 98.67% accuracy in classification, while the proposed method achieved 99.12% accuracy. This demonstrated the effectiveness of combining feature extraction with CNN for tumor detection compared to using CNN alone. The high accuracy of the proposed method could help physicians accurately diagnose tumors and improve patient outcomes.
This document provides an overview of digital image processing. It defines what an image is, noting that an image is a spatial representation of a scene represented as an array of pixels. Digital image processing refers to processing digital images on a computer. The key steps in digital image processing are image acquisition, enhancement, restoration, compression, morphological processing, segmentation, representation, and recognition. Digital image processing has many applications including medical imaging, traffic monitoring, biometrics, and computer vision.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
This document describes a system for detecting brain tumors in MRI images using image segmentation. It discusses how existing manual detection of tumors is difficult due to noise and requires many days. The proposed system applies preprocessing like filtering and grayscale conversion. It then uses image segmentation techniques to detect tumor edges and boundaries. Features are extracted and classification is used to differentiate between normal and tumor images, helping doctors detect tumors earlier. The system is implemented in MATLAB and aims to overcome difficulties in early tumor detection.
Application of-image-segmentation-in-brain-tumor-detectionMyat Myint Zu Thin
This document discusses applications of image segmentation in brain tumor detection. It begins by defining brain tumors and different types. It then discusses various image segmentation methods that can be used for brain tumor segmentation, including k-means clustering, region-based watershed algorithm, region growing, and active contour methods. It demonstrates how these methods can be implemented in Python for segmenting tumors from MRI images. The document also discusses computer-aided diagnosis systems and the roles of artificial intelligence and machine learning in medical image analysis and cancer diagnosis using image processing.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Brain Tumor Detection Using Image ProcessingSinbad Konick
The process of brain tumor detection using various filters and finding out the best possible approach. Processing the image and using other filters and find out the result.
Breast Cancer Detection with Convolutional Neural Networks (CNN)Mehmet Çağrı Aksoy
Photos and various addresses are taken from the internet. It may be subject to copyright.
For references:
https://github.com/mcagriaksoy/EEM-305-Signals-and-Systems
https://medium.com/intro-to-artificial-intelligence/deep-learning-series-1-intro-to-deep-learning-abb1780ee20
This document discusses color image processing and different color models. It begins with an introduction and then covers color fundamentals such as brightness, hue, and saturation. It describes common color models like RGB, CMY, HSI, and YIQ. Pseudo color processing and full color image processing are explained. Color transformations between color models are also discussed. Implementation tips for interpolation methods in color processing are provided. The document concludes with thanks to the head of the computer science department.
This document discusses various image compression standards and techniques. It begins with an introduction to image compression, noting that it reduces file sizes for storage or transmission while attempting to maintain image quality. It then outlines several international compression standards for binary images, photos, and video, including JPEG, MPEG, and H.261. The document focuses on JPEG, describing how it uses discrete cosine transform and quantization for lossy compression. It also discusses hierarchical and progressive modes for JPEG. In closing, the document presents challenges and results for motion segmentation and iris image segmentation.
This document provides an overview of image enhancement techniques. It discusses the objectives of image enhancement, which is to process an image to make it more suitable for a specific application or task. The document focuses on spatial domain techniques for image enhancement, specifically point processing methods and histogram processing. It categorizes image enhancement methods into two broad categories: spatial domain methods, which directly manipulate pixel values; and frequency domain methods, which first convert the image into the frequency domain before performing enhancements.
IRJET- Image Processing based Lung Tumor Detection System for CT ImagesIRJET Journal
This document presents a method for detecting lung tumors in CT scan images using image processing techniques. The proposed method involves preprocessing images using median filtering for noise removal and contrast adjustment for enhancement. The lungs are then segmented from the images using mathematical morphology. Geometric and textural features are extracted from the segmented region of interest and used as input for an SVM classifier to detect lung cancer. The methodology was tested on a dataset from The Cancer Imaging Archive and was able to successfully detect lung tumors in CT images.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
An Introduction to Image Processing and Artificial IntelligenceWasif Altaf
This document provides an introduction to image processing and artificial intelligence. It defines what an image is from different perspectives including in literature, general terms, and in computer science as an exact replica of a storage device. It describes image processing as analyzing and manipulating images with three main steps: importing an image, manipulating or analyzing it, and outputting the result. It also discusses what noise is in images, methods to remove noise, color enhancement techniques, sharpening images to increase contrast, and segmentation and edge detection.
This document discusses various types of medical imaging technologies. It describes radiologic/x-ray technology, ultrasound technology, CT scans, MRI scans, and nuclear imaging including PET and SPECT. The goal of medical imaging is to non-invasively examine the inside of the body to diagnose health problems and guide treatment. Each technology has advantages for certain applications based on the type of information and depth of imaging it provides. Together these modalities provide physicians a variety of tools to accurately diagnose and monitor patient health issues.
Breast cancer diagnosis machine learning pptAnkitGupta1476
This document summarizes a presentation on using machine learning to diagnose breast cancer. It introduces machine learning and explains that it uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. It then provides an overview of breast cancer, risk factors, and statistics. It states that machine learning will be used to analyze breast cancer biopsy data to make diagnoses. The document outlines the steps of collecting and exploring the biopsy data, preparing training and test datasets, training a k-nearest neighbors model on the data, and calculating the model's accuracy on the test data using a confusion matrix.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
Brain Tumor Detection Using Deep Neural Network.pptxAbdulRehman417114
The document summarizes a research paper that proposed a new method for detecting brain tumors using MRI images. The method combines a clustering algorithm for feature extraction with a convolutional neural network (CNN). When applied to a dataset of MRI images, the CNN alone achieved 98.67% accuracy in classification, while the proposed method achieved 99.12% accuracy. This demonstrated the effectiveness of combining feature extraction with CNN for tumor detection compared to using CNN alone. The high accuracy of the proposed method could help physicians accurately diagnose tumors and improve patient outcomes.
This document provides an overview of digital image processing. It defines what an image is, noting that an image is a spatial representation of a scene represented as an array of pixels. Digital image processing refers to processing digital images on a computer. The key steps in digital image processing are image acquisition, enhancement, restoration, compression, morphological processing, segmentation, representation, and recognition. Digital image processing has many applications including medical imaging, traffic monitoring, biometrics, and computer vision.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
This document describes a system for detecting brain tumors in MRI images using image segmentation. It discusses how existing manual detection of tumors is difficult due to noise and requires many days. The proposed system applies preprocessing like filtering and grayscale conversion. It then uses image segmentation techniques to detect tumor edges and boundaries. Features are extracted and classification is used to differentiate between normal and tumor images, helping doctors detect tumors earlier. The system is implemented in MATLAB and aims to overcome difficulties in early tumor detection.
Application of-image-segmentation-in-brain-tumor-detectionMyat Myint Zu Thin
This document discusses applications of image segmentation in brain tumor detection. It begins by defining brain tumors and different types. It then discusses various image segmentation methods that can be used for brain tumor segmentation, including k-means clustering, region-based watershed algorithm, region growing, and active contour methods. It demonstrates how these methods can be implemented in Python for segmenting tumors from MRI images. The document also discusses computer-aided diagnosis systems and the roles of artificial intelligence and machine learning in medical image analysis and cancer diagnosis using image processing.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Brain Tumor Detection Using Image ProcessingSinbad Konick
The process of brain tumor detection using various filters and finding out the best possible approach. Processing the image and using other filters and find out the result.
Breast Cancer Detection with Convolutional Neural Networks (CNN)Mehmet Çağrı Aksoy
Photos and various addresses are taken from the internet. It may be subject to copyright.
For references:
https://github.com/mcagriaksoy/EEM-305-Signals-and-Systems
https://medium.com/intro-to-artificial-intelligence/deep-learning-series-1-intro-to-deep-learning-abb1780ee20
This document discusses color image processing and different color models. It begins with an introduction and then covers color fundamentals such as brightness, hue, and saturation. It describes common color models like RGB, CMY, HSI, and YIQ. Pseudo color processing and full color image processing are explained. Color transformations between color models are also discussed. Implementation tips for interpolation methods in color processing are provided. The document concludes with thanks to the head of the computer science department.
This document discusses various image compression standards and techniques. It begins with an introduction to image compression, noting that it reduces file sizes for storage or transmission while attempting to maintain image quality. It then outlines several international compression standards for binary images, photos, and video, including JPEG, MPEG, and H.261. The document focuses on JPEG, describing how it uses discrete cosine transform and quantization for lossy compression. It also discusses hierarchical and progressive modes for JPEG. In closing, the document presents challenges and results for motion segmentation and iris image segmentation.
This document provides an overview of image enhancement techniques. It discusses the objectives of image enhancement, which is to process an image to make it more suitable for a specific application or task. The document focuses on spatial domain techniques for image enhancement, specifically point processing methods and histogram processing. It categorizes image enhancement methods into two broad categories: spatial domain methods, which directly manipulate pixel values; and frequency domain methods, which first convert the image into the frequency domain before performing enhancements.
IRJET- Image Processing based Lung Tumor Detection System for CT ImagesIRJET Journal
This document presents a method for detecting lung tumors in CT scan images using image processing techniques. The proposed method involves preprocessing images using median filtering for noise removal and contrast adjustment for enhancement. The lungs are then segmented from the images using mathematical morphology. Geometric and textural features are extracted from the segmented region of interest and used as input for an SVM classifier to detect lung cancer. The methodology was tested on a dataset from The Cancer Imaging Archive and was able to successfully detect lung tumors in CT images.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
This document is a project report submitted for a Bachelor of Technology degree. It discusses developing a system to detect lung cancer from CT scan images using deep learning techniques in MATLAB. The project aims to enhance CT images, segment images, extract features, and classify cancers as benign or malignant to detect lung cancer at an early stage for increased survival rates. It outlines the hardware, software, and dataset requirements and discusses the design, image processing operations to be used, and objectives to accurately detect lung cancer parameters.
Detection of Diverse Tumefactions in Medial Images by Various Cumulation MethodsIRJET Journal
This document proposes and compares two methods - Particle Swarm Optimization (PSO) and Search Based Optimization - for detecting tumors in MRI and CT medical images. It first reviews previous work using techniques like PSO, cuckoo search, and evolutionary convolutional neural networks for tumor detection. It then describes the methodology, which involves preprocessing images, segmenting them using PSO and Search Based Optimization, classifying segments as tumor or non-tumor using Support Vector Machines, and extracting features to identify the tumor. Parameters like accuracy, processing time, and error are compared between the two optimization methods to determine which achieves a more accurate tumor shape detection.
Multiple Analysis of Brain Tumor Detection based on FCMIRJET Journal
This document summarizes a research paper that proposes a method for detecting brain tumors in MRI images using fuzzy c-means clustering. It begins with an introduction to brain tumors and MRI imaging. It then describes the proposed method which includes pre-processing the MRI images, segmenting the images using fuzzy c-means clustering to identify tumor regions, extracting features using fuzzy rules, and analyzing the results to determine tumor size and location. The method is compared to previous work and shown to improve accuracy, precision, and recall in brain tumor detection. In conclusion, preprocessing helps identification, fuzzy c-means segmentation identifies tumor pixels, and the overall method can detect and analyze brain tumors in MRI images.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Multiple Analysis of Brain Tumor Detection Based on FCMIRJET Journal
The document proposes a system to detect brain tumors in MRI images using multiple steps including pre-processing, segmentation using fuzzy c-means clustering, and feature extraction using fuzzy rules. It discusses how pre-processing improves tumor detection, fuzzy c-means segmentation identifies tumor regions and size, and prior approaches have limitations. The proposed system aims to better detect and identify brain tumors in MRI images as compared to other algorithms.
A Review On Lung Cancer Detection From CT Scan Images Using CNNDon Dooley
The document reviews various methodologies for detecting lung cancer from CT scan images, finding that convolutional neural networks along with image processing provide the most suitable approach. It discusses related work applying techniques like image enhancement, segmentation, and machine learning classification to identify cancerous nodules. Recent approaches using deep learning, specifically 3D convolutional neural networks, achieve high accuracy rates of 94-96% for cancer detection and classification.
IRJET- Lung Cancer Detection using Digital Image Processing and Artificia...IRJET Journal
This document discusses a proposed system to detect lung cancer at early stages using digital image processing and artificial neural networks. The system consists of several steps: image acquisition, preprocessing using histogram equalization, segmentation using thresholding, dilation, image filling, feature extraction from CT images, and classification of images using an artificial neural network. The goal is to develop an automated diagnostic system that can maximize the detection of true positive lung cancer cases while minimizing false negatives to improve early detection rates and patient outcomes.
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
IRJET- A New Strategy to Detect Lung Cancer on CT ImagesIRJET Journal
This document presents a new strategy for detecting lung cancer on CT images using image processing techniques. It involves acquiring CT scan images, preprocessing the images through techniques like grayscale conversion and Gabor filtering, segmenting the images using adaptive thresholding, extracting regions of interest through feature extraction methods like GLCM, and classifying images as cancerous or normal using support vector machines (SVM) and backpropagation neural networks (BPNN). The methodology achieves 96.32% accuracy for SVM and 83.07% accuracy for BPNN in detecting lung cancer from CT images.
IRJET - Lung Cancer Detection using GLCM and Convolutional Neural NetworkIRJET Journal
This document summarizes a research paper that proposes a method for detecting lung cancer using CT scan images with convolutional neural networks. The method involves preprocessing images using median filtering to remove noise, segmenting images using k-means clustering, extracting features using gray-level co-occurrence matrix, and classifying images using convolutional neural networks. The researchers achieved 96% accuracy in classifying tumors as malignant or benign, which is more accurate than traditional neural network methods.
IRJET - Detection of Brain Tumor from MRI Images using MATLABIRJET Journal
This document presents a method for detecting brain tumors in MRI images using MATLAB. It involves pre-processing the MRI images to reduce noise and enhance contrast. Thresholding and watershed segmentation are then used to segment the images and isolate the tumor region. Morphological operations like erosion and dilation are applied post-segmentation to extract the tumor boundaries. The algorithm is tested on sample MRI images and is able to accurately detect tumors in all cases. The automated method provides faster and more consistent tumor detection compared to manual segmentation and reduces processing time.
INVESTIGATION THE EFFECT OF USING GRAY LEVEL AND RGB CHANNELS ON BRAIN TUMOR ...csandit
Analysis the effect of using gray level on the Brain tumor image for improving speed of object
detection in the field of Medical Image using image processing technique. Specific areas of
interest are image binarization method, Image segmentation. Experiments will be performed by
image processing using Matlab. This paper presents a strategy for decreasing the calculation
time by using gray level and just one channel Red or Green or Blue in medical Image and
analysis its impact in order to improve detection time and the main goal is to reduce time
complexity.
This document summarizes a research paper that developed a method for detecting lung cancer using MATLAB. The method involved pre-processing CT scan images by resizing and converting them to grayscale. Image enhancement techniques like median filtering were applied to reduce noise. The images were then segmented and features were extracted using techniques like thresholding and watershed transforms. Binarization and masking approaches were used to classify images based on pixel counts and detect abnormal masses indicating cancer. The method achieved true positive rates of 92.86% for binarization and 85.7% for masking, demonstrating the potential of this automated process to help diagnose lung cancer from CT scans.
Detection of Lung Cancer using SVM ClassificationIRJET Journal
This document presents a method for detecting lung cancer using support vector machine (SVM) classification of sputum cell images. The authors first extract features from sputum cell images such as nucleus-cytoplasm ratio, perimeter, density, curvature, and circularity. They then use these extracted features to train an SVM classifier to classify sputum cells as cancerous or normal. The authors test their proposed method on 100 sputum cell images and evaluate the technique's performance using metrics like sensitivity, precision, specificity, and accuracy. Their results indicate the SVM classification approach shows potential for early detection of lung cancer from sputum cell analysis.
IRJET - Liver Cancer Detection using Image ProcessingIRJET Journal
The document describes a study that aims to detect liver cancer at an early stage using image processing techniques on computed tomography (CT) scans. The researchers propose a method involving three main steps: 1) preprocessing the CT images using anisotropic diffusion filters to reduce noise, 2) processing the images using morphological operations like dilation and erosion to segment and detect tumor regions, and 3) highlighting the identified tumor regions on the original CT images for clear observation. The study explores using common image processing methods like filtering, segmentation, and highlighting to analyze medical images and help identify liver cancers earlier for improved treatment outcomes.
Analysis Of Medical Image Processing And Its Application In HealthcarePedro Craggett
This document summarizes a research paper that analyzes medical image processing and its applications in healthcare. It discusses how medical image processing is an emerging field that can help with medical diagnosis. The paper focuses on detecting and extracting tumors from MRI scans of the brain using MATLAB. It describes preprocessing MRI images, performing segmentation, removing noise, and applying morphological operations. The goal is to accurately detect and extract tumor cells to help physicians with diagnosis. The techniques discussed include filtering, enhancement, and classification of features to analyze abnormal cells in MRI images.
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
A novel CAD system to automatically detect cancerous lung nodules using wav...IJECEIAES
A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
Similar to Lung Cancer Detection on CT Images by using Image Processing (20)
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
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Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
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Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Make a Field Mandatory in Odoo 17Celine George
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Lung Cancer Detection on CT Images by using Image Processing
1. @ IJTSRD | Available Online @ www.ijtsrd.com
ISSN No: 2456
International
Research
Lung Cancer Detection on CT Images
Bindiya Patel1
, Dr. Pankaj
1
Department of Digital Electronics
1,2
Rungta College of Engineering and
ABSTRACT
This project is mainly based on image processing
technique. In this work MATLAB have been used
through every procedure made. Image processing
techniques are widely use in bio-medical sector. The
objective of our work is noise removal operation,
thresholding, gray scale imaging, histogram
equalization, texture segmentation, and morphological
operation. Detection of lung cancer from computed
tomography (CT) images is done by using MATLAB
software. By using these methods the work has been
done on CT images and the final tumor area has been
shown with pixel values.
Keywords: Image Acquisition, Image enhancement,
Image Segmentation, Morphological operation
I. INTRODUCTION
In this project we are detecting the lung cancer from
the computed tomography (CT) images by using
image processing technique in MATLAB. First of all
we must know that what lung cancer is, so Lung
cancer is a disease in which abnormal cells
multiplying and growing and forms a tumor in lungs.
There are different types of tumor and not all tumors
are cancerous some are the basic tumor which can be
cure by some basic treatments. Also Some cancer
cells can be spread to other body parts and some are
not for example: - Survival Cancer cells can be
carried away from the lungs in blood, or lymph fluid
that surrounds with lung tissue, on the other hand
benign tumors are the tumors which do not spread to
other part of the body. There are several types of lung
cancer and these are mainly divided into two main
categories this are: - small cell and non
lung cancer. But people do have a higher chance of
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 3 | Mar-Apr 2018
ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume
International Journal of Trend in Scientific
Research and Development (IJTSRD)
International Open Access Journal
Lung Cancer Detection on CT Images by using Image Processing
Dr. Pankaj Kumar Mishra2
, Prof. Amit Kolhe
Electronics, 2
Department of Electronics and Telecommunication
ngineering and Technology, Bhilai, Chhattisgarh
This project is mainly based on image processing
technique. In this work MATLAB have been used
through every procedure made. Image processing
medical sector. The
objective of our work is noise removal operation,
g, gray scale imaging, histogram
equalization, texture segmentation, and morphological
operation. Detection of lung cancer from computed
tomography (CT) images is done by using MATLAB
software. By using these methods the work has been
the final tumor area has been
Image Acquisition, Image enhancement,
Image Segmentation, Morphological operation
In this project we are detecting the lung cancer from
the computed tomography (CT) images by using
image processing technique in MATLAB. First of all
we must know that what lung cancer is, so Lung
cancer is a disease in which abnormal cells
growing and forms a tumor in lungs.
There are different types of tumor and not all tumors
are cancerous some are the basic tumor which can be
cure by some basic treatments. Also Some cancer
cells can be spread to other body parts and some are
Survival Cancer cells can be
carried away from the lungs in blood, or lymph fluid
that surrounds with lung tissue, on the other hand
benign tumors are the tumors which do not spread to
other part of the body. There are several types of lung
nd these are mainly divided into two main
small cell and non-small cell
lung cancer. But people do have a higher chance of
survival from the lung cancer if the cancer can be
detected in the early stages. Survival from lung cancer
is directly related to its speed of growth and at its
detection time as soon as it can be detected the chance
of survival will be increase. This project is starts with
collecting a number of computed tomography (CT)
scanned images from the available data base
images will be further being processed, enhanced, and
segmented than load the images into mat lab for
cancer detection and then after comparison classify
into normal and abnormal tumor. This techniques
helps to detects cancer and help us for diagno
solution. This computed tomography (CT) scanned
images are used as an input image, after getting the
input image we removed the noise from the input
image by using different filtration technique. In next
step we do the gray scale imaging and then
thresholding operation is done and after that we apply
the histogram equalization, these all above operations
are come under the image acquisition and image
enhancement. In next step image segmentation will be
done the segmentation is done, there are different
types of image segmentation are available. We are
computing the texture segmentation technique to the
image. After that we do the morphological operation
to the image so that we can get a clear and accurate
region of the tumor. We are aiming to get more
accurate result by using image enhancement technique
and image segmentation operation and by the
comparison of effected area so that intensity of cancer
can be classified. We can also use MRI images, X
images of lung for the cancer detection as an input
image instead of using computed tomography (CT) .
Apr 2018 Page: 2525
6470 | www.ijtsrd.com | Volume - 2 | Issue – 3
Scientific
(IJTSRD)
International Open Access Journal
sing Image Processing
Kolhe2
Department of Electronics and Telecommunication
Chhattisgarh, India
survival from the lung cancer if the cancer can be
detected in the early stages. Survival from lung cancer
directly related to its speed of growth and at its
detection time as soon as it can be detected the chance
of survival will be increase. This project is starts with
collecting a number of computed tomography (CT)
scanned images from the available data base. This
images will be further being processed, enhanced, and
segmented than load the images into mat lab for
cancer detection and then after comparison classify
into normal and abnormal tumor. This techniques
helps to detects cancer and help us for diagnosis
solution. This computed tomography (CT) scanned
images are used as an input image, after getting the
input image we removed the noise from the input
image by using different filtration technique. In next
step we do the gray scale imaging and then
holding operation is done and after that we apply
the histogram equalization, these all above operations
are come under the image acquisition and image
enhancement. In next step image segmentation will be
done the segmentation is done, there are different
types of image segmentation are available. We are
computing the texture segmentation technique to the
image. After that we do the morphological operation
to the image so that we can get a clear and accurate
region of the tumor. We are aiming to get more
curate result by using image enhancement technique
and image segmentation operation and by the
comparison of effected area so that intensity of cancer
We can also use MRI images, X-ray
images of lung for the cancer detection as an input
image instead of using computed tomography (CT) .
2. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 3 | Mar-Apr 2018 Page: 2526
2. METHODOLOGY
This project consists of four major stages, the first
stage is Image Acquisition, the second stage is Image
Processing techniques, third stage is consist of image
segmentation operation and the fourth or last stage is
image extraction, and comparison. All the four stages
are having some basic operations and steps which are
necessary to full fill the requirements and to complete
the stage step by step.
Block diagram of proposed system is shown below:-
The description of all the stages and steps are given
below:-
A.)IMAGE ACQUISITION
The first stage of any image processing system
involves image acquisition, after the image has been
obtained further operations are applied. The aim of
image acquisition is to get the image of required area
or effected region so that the detection can be done. It
starts with collecting a computed tomography (CT)
images of lung of different person from the record or
available data base. This computed tomography (CT)
images are further used as input to the system. After
image acquisition we can proceed to image processing
stage for further operations.
FIGURE1-LUNG CT IMAGE
B.) IMAGE ENHANCEMENT
The second stage is an image enhancement. Image
enhancement is a technique which is used to improve
the quality of the image and to get the better image
than the provided one, it provides a clear better and
the accurate parameter of the desired region. For this
purpose noise removal from the images, image
filtering, techniques are use, which will helpful to
detect cancer parameter during processing. Image
processing involves two main steps that are; image
enhancement technique and image segmentation
technique both are having their own properties and
important role for improving the quality of the image.
Both the process having a different- different
technique for the image enhancement and
segmentation for the more accurate result the best one
will be choose. In this stage we use the different
techniques to make the image better and enhance it
from noising, corruption or interference.
Enhancement technique provides better input for other
automated image processing technique. For image
enhancement first we use different types of filtration
methods for the removal of noise from the image Ex
Linear filter, median filter, high pass filter and
adaptive filter, in next step thresholding operation has
been done and then we convert the input image to
gray scale image.
a.) DE-NOISING
Digital images can have various types of noise. This
noise can be the result of error in the image
processing and segmentation and some other further
operations that result in the pixel values that do not
true intensity of real image. This noise may leads to
interrupted o false values which may give the false
information about the tumor and the person can be
misguided so the removal of noise is necessary. There
are several ways by which the noise can be introduced
into the images, depending on the image is created for
example: - (1) noise can be the result of damaged
3. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 3 | Mar-Apr 2018 Page: 2527
computed tomography scanned film or introduced by
scanner itself. (2) The another reason can be the
mechanism for gathering the data. (3)An electronic
transmission of image data can introduce the noise
etc. As we know the removal of noise is very
important so we use different types of filtration
method to remove the noise from the picture for
example removal of noise by linear filter, removal of
noise from an average filter and median filter,
removal of noise by using adaptive filter. We are
using median filter for the removal of noise, but we
can use any of the method from above three. The
median filter is a nonlinear digital filtering technique,
often used to remove noise. Such noise reduction is a
typical pre-processing step to improve the results of
later processing for example, edge detection on an
image. In median filtering the value of an output pixel
is determined by the median of neighbourhood pixels,
rather than the mean values of the pixels. The median
is much less sensitive than the mean to extreme values
therefore it is better able to remove outliers without
reducing the sharpness of the image. So that Median
filtering is very widely used in digital image
processing because, under certain conditions, it
preserves edges while removing noise. The main idea
of the median filter is to run through the signal entry
by entry, replacing each entry with the median of
neighbouring entries. The pattern of neighbours is
called the "window", which slides, entry by entry,
over the entire signal. For 1D signal, the most obvious
window is just the first few preceding and following
entries, whereas for 2D (or higher-dimensional)
signals such as images, more complex window
patterns are possible (such as "box" or "cross"
patterns). The concept of median filter is that if the
window has an odd number of entries, then the
median is simple to define: it is just the middle value
after all the entries in the window are sorted
numerically. For an even number of entries, there is
more than one possible median. This filter enhances
the quality of the MRI image. (REFERENCE-
International Journal of Electronics, Communication
& Soft Computing Science and Engineering Rajesh
C.Patil, Dr. A. S. Bhalchandra ISSN: 2277-9477,
Volume2, Issue1). In our project we are using the
median filter for the removal of noise.
FIGURE2- FILTERED IMAGE
b.) GRAYSCAL IMAGING
Computed tomography (CT) scanned images are
combination of a series of x-ray images taken from
the different angles and uses some computational
processing to create cross sectional images of
specified area or the image of required body part. The
computed tomography (CT) images are black and
white images in general. When we take these images
as input images on computer, computer considers
these images as a black and white image. So we apply
gray scale imaging to the image.
Gray scaled images are not like simple black and
white images it provides a combination of black and
white or we can say a gray shade instead of providing
only two shades black and white. On images gray
scale or grey scale is one in which the amount of each
pixel is single sample represents the amount of light it
contains or we can say that, it carries only the
intensity information. These are from black and white
to exclusive shades of gray, varying from black at the
lowest intensity to white at the highest intensity. Gray
scale images are distinct from bit by bit on black and
white images. The illusion of gray scale shading in a
half tone image is obtain by rendering the image as a
combination of black dots on white background or
vice versa, and these gray scale images are result of
measuring the intensity of light at each pixel
according to a particular weighted combination of
frequencies or wavelengths.
The RGB is a primary color brightness level in RGB
are represented in number form as 0 to 255 in analog
or we can say it has 255 level and in digital it
represents in binary form as 00000000 to 11111111,
where black is represented by R=B=G=0 or
R=G=B=00000000, and white is represented by
R=G=B=1 or R=G=B=1. Because there is 8bit in
binary representation of the gray level so that it is also
called as 8-bit gray scale. Array of class uint8, uint16,
int16, single or double whose pixel values specify
intensity value. For single or double arrays, values
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range from [0, 1]. For uint8, values range from
[0,255]. For uint16, values range from [0, 65535]. For
int16, values range from [-32768, 32767]. At present,
the most commonly used storage method is 8-bit
storage, which have 256gray level intensity of each
pixel can have from 0 to 255, with 0 being black and
255 being white. (REFERENCE- “A theory based on
conversion of RGB image to Gray image” by- Tarun
Kumar and Karun verma, computer science and
engineering department, @ International journal of
computer application sep2010).
FIGURE3- GRAY SCALE IMAGE
C.) THRESHOLDING
Image thresholding is a simple way of partitioning an
image into a foreground and background. Common
image thresholding algorithms include histogram and
multilevel thresholding. Now we know the main
purpose of thresholding, so the working of
thresholding is – as we know the simplest property
that pixels in a region can share is intensity. So,
thresholding operation segments such regions and
separate light and dark regions. It creates binary
images from grey-level ones by turning all pixels
below some threshold to zero and all pixels about that
threshold to one or apart the dark and lighter area
from each other. Let’s assume if g(x, y) is a threshold
version of f (x, y) at some global threshold ‘T’ that
separates these modes. Then any point (x, y) for
which f(x, y) > T is called any object point; otherwise
it is back ground point. High intensity areas mostly
Comprises of cancer cell.(REFERENCE-
International Journal of Emerging Technology and
Advanced Engineering Website: www.ijetae.com,
ISSN 2250-2459, ISO 9001:2008 Certified Journal,
Volume 7, Issue 7, July2017). Image thresholding is
most effective in images with high level of contrast.
This image analysis technique is a type of image
segmentation that isolates objects by converting Gray
scale images into binary images which is the next step
of our project.
1) IMAGE SEGMENTATION
Image segmentation is an essential process for most
image analysis subsequent tasks. In particular, many
of the existing techniques for image description and
recognition depend highly on the segmentation results
Segmentation divides an image into its constituent
regions or objects as well as it can detect the edge of
the images. Image segmentation is a technique which
is used for separating the image from the background
as well as from each other or we can say that to
separate the image we are determining the outline of
the image using threshold operation., this process is
done by classified the pixels into objects. To divide
and segment the enhanced image generally histogram
equalization, threshold segmentation, region based
segmentation method and either watershed
approaches or texture segmentation can be used here
we are using histogram technique and after histogram
we will go for texture segmentation technique.
a.)HISTOGRAM TECHNIQUE
Histogram equalization technique is used for the
segmentation of the image; it is one of the most
effective techniques for segmentation. Histogram
equalization of an image shows the pixels intensity
values. For example generally it forms a graph in
which x-axis shows the gray level intensities and the
y-axis shows the frequency of these intensities. In
general, a histogram is the estimation of the
probability distribution of a particular type of data. An
image histogram is a type of histogram which offers a
graphical representation of the tonal distribution of
the grey values in a digital image. To improve the
contrast of the image through histogram equation, it
spreads out intensity values along the total range of
value in order to achieve higher contrast. The methods
of histogram equation are: histogram expansion, local
area histogram equalization (LAHE), cumulative
histogram equalization, par sectioning, and odd
sectioning. (REFERENCE- Histogram Equalization,
by- Robert Krutch and David Tenorio,
Microcontroller Solution group Guadalajara@ June
2011, free scale semiconductor, Inc.) The histogram
can have many uses in image processing apart from
image segmentation for example it can be used for
image processing, can be used for brightness purpose
not only for brightness purpose can also be used for
adjusting the contrast level, and last but no not the
least it is widely used for segmentation.
b.)TEXTURE SEGMENTATION
The texture is most important attribute in many image
analysis or computer vision applications. It is a set of
metrics calculated in image processing to quantify the
5. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
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texture of an image. Texture of image gives us
information the spatial arrangement of colour or
selected region of an image. The procedures
developed for texture problem can be subdivided into
four categories: structural approach, statistical
approach, model based approach and filter based
approach. Different definitions of texture are
described, but more importance is given to filter based
methods. Such as Fourier transform, Gabor,
Thresholding, Histogram and wavelet transforms. An
image texture can be used in segmentation or
classification of an image, or to extract boundaries
between major texture regions. For more accurate
result in segmentation the most useful features are
spatial frequency and an average gray level. Texture
is a difficult concept to represent. The identification
of specific textures in an image is achieved primarily
by modelling texture as a two-dimensional gray level
variation. The relative brightness of pairs of pixels is
computed such that degree of contrast, regularity,
coarseness and directionality. There are two main
types of texture segmentation that are region based
and boundary based texture segmentation.
Region Based- it attempts to group or cluster pixels
based on texture property. Segmentation algorithms
operate iteratively by grouping together pixels which
are neighbours and have similar values and splitting
groups of pixels which are dissimilar in value.
Boundary Based- Edges contain some of the most
useful information in an image. We may use edges to
measure the size of objects in an image; to isolate
particular objects from their background; to recognize
or classify objects. In boundary based it attempts to
group or cluster pixels based on edges between pixels
that come from different texture properties.
(REFERENCE- Texture segmentation: different
methods, by- Vaijinath V. Bhosle, V Rushsen P.
Pawar @ nov2013 International journal of soft
computing and engineering.)
FIGURE4- TEXTURE SEGMENTATION
D.) MORPHOLOGICAL OPERATION
This is the last step for the detection of lung cancer.
This stage is an important stage that uses algorithms
and techniques to detect and isolate various desired
portions or shapes of a given image. It is used to
predict the probability of lung cancer presence when
the input data to an algorithm is too large to be
processed and it is suspected to be notoriously
redundant, then the input data will be transformed into
a reduced representation set of features. From all of
the above steps like image processing, image
segmentation, we get the clear image of the tumor
region in lung, so differentiate the tumor in lung are
called morphological operation. The basic characters
for the morphological operation are area for which the
numbers of iterations are performed. This are the
values which we calculate or the area or region of the
tumor which we are obtained from enhanced and
segmented images and also from morphological or
thresholding. These features are measured in scalar.
After getting the tumor region we compare the tumor
with the standards and try to find the type of the
tumor and from the size of the tumor we try to find
the stage of the cancer, because from all of this
information are very important because it will use in
the treatment of the cancer and from this information
the required steps and cure will be taken for example
Lung nodule is defined as smallest growths in the
lung that measure between 5mm to 25mm in size.
Malignant nodules tend to be bigger in size >25mm,
and have a faster growth rate. In the normal images
nodule size is less than 25mm. And in the abnormal
images its size is greater than 25mm. With the help of
classifications and comparison in the classification
stage Tumor is classified as normal Cancer Tumor or
abnormal Cancer Tumor.
FIGURE5- FINAL TUMOR AREA
3. RELEATED WORK
6. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
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AUTHOR IMAG
E
TECHNIQUE ACCUR
ACY
Siva Sakthi,
Kumar
Parasuraman,
Arumuga
Maria Devi
CT Otsu threshold,
watershed
segmentation
90.90%
S.shiva
kumar
CT SVM,RBF
kernel
80.36%
Disha
sharma,
Gagandeep
Jindal
CT CAD, Weiner
filter
80%
Aniket
Gaikwad,
Azharuddin
Inamdar,
Vikas Behera
CT Histogram and
watershed
segmentation
84.55%
M.
Premchander,
Dr. .M.
Venkateshwa
ra, dr. T.V.
Rajinikanth
CT Gabor filter,
watershed
segmentation
86.39%
Anuradha S.
Deshpande,
Dhanesh D.
Lokhande,Ra
hul P.
Mundhe,
Juilee
M.Ghatole
CT,
MRI
Watershed
segmentation,
SVM algorithm
90.90%
J.R Marsilin CT SVM algorithm 78.00%
Yaoying
Huang,
Wangsen Li,
Xiaojiaoye
CT Genetic
algorithm,
feature
selection
99.1%
Fatma Taher Sputu
m
Bayesian 88.62%
Afazan Adam CT Genetic
algorithm, back
propagation
neural network
83.86%
Yang Hiu CT SVM(GBRF
kernel type)
87.82%
4. RESULT AND DISCUSSION
As we know lung cancer is one of the most dangerous
diseases in the world. An image improvement
technique is developing for earlier disease detection
and treatment stages. Correct Diagnosis and early
detection of lung cancer can increase the survival rate.
Image quality and accuracy is the core factors of this
research, image quality assessment as well as
enhancement stage. This project is based on the
processing of computed tomography (CT) images. We
also conclude that the lung cancer can be detected in
an early stage by using any one of this method and by
following all the four steps mentioned above. As we
can see the median filter is chosen. After image
processing we go for segmentation approach here we
use histogram and texture segmentation. After
segmentation of image, morphological operation is
used to get individual lung and to eliminate
unnecessary parts. By doing morphological
operations, we get not only the individual lung but
also apparent the lung nodule then we extracts the
Tumor by comparison. The area calculated by the
process is 1488 pixels. In this the resulting tumours
are of different dimensions by measuring the area of
Tumor, so the lung cancer stage can be detected
accurately in early stage using the proposed
methodology cancer detection and respective
diagnosis measure which will helps to clear cancer
Parameters permanently. The result are analysed
graphically as well as numerically.
5. FUTURE SCOPE
We are aiming to get the more accurate results by
using various enhancement and segmentation
technique, different segmentation strategies and
calculations are the root idea of digital image
processing the more accurate result will be more
helpful and good for the diagnosis solution and the
person can have more chances of survival from this
dangerous disease and we can do one more thing apart
from using different strategies we can use a fusion
method of all this techniques or the hybrid methods
to get the more accurate result. We can also develop
the system as a real time system in future. It means
the system will work at the time of diagnosis as well
as with the time when we take the computed
tomography (CT) images, the advantage of the real
time system will be that it helps the person to cure the
disease as soon as possible and provides a help for
early treatment so the survival chance can be increase.
In future by parameter and area calculation of the
7. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
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tumor at the time of detection we can also find that
tumor has been in which stage.
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