1) The document compares two machine learning algorithms, probabilistic neural network (PNN) and support vector machine (SVM), for detecting breast cancer in mammogram images.
2) It evaluates the performance of PNN and SVM on a dataset of 322 mammogram images containing both benign and malignant tumors.
3) The proposed methodology applies techniques like image enhancement, segmentation, and feature extraction before classifying the images using PNN and SVM to detect tumors and determine if they are benign or malignant.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
The document discusses computer assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. It begins with an introduction to breast cancer and computer aided detection and diagnosis systems. It then provides background on areas of interest including improvement of pictorial information and machine vision. Next, it discusses microcalcifications, mammography, and mammograms. The document reviews literature on various preprocessing, feature extraction, and detection techniques. It identifies challenges in microcalcification detection including their small size and variable clusters. Finally, it outlines the plan of action for the thesis including use of the mini-MIAS mammogram database and a range of techniques to remove pectoral muscle and x-ray labels.
Segmentation of thermograms breast cancer tarek-to-slid shareTarek Gaber
This document presents a new method for segmenting regions of interest (ROIs) in breast thermograms to detect breast abnormalities. The method uses features extracted from the ROIs, like statistical and texture features, and supports vector machines for classification. It was tested on a database of 149 patients, achieving 100% accuracy in detecting normal vs. abnormal breasts. The method provides an automatic and low-cost approach to segmenting thermograms for breast cancer detection.
3D mammography is a new technology that produces 3D images of breast tissue by combining multiple x-ray images taken from different angles. Clinical trials have found that 3D mammography increases cancer detection rates by 47% compared to standard 2D mammography. It also reduces unnecessary patient recalls by 28-40% by minimizing issues with dense breast tissue. The enhanced 3D images allow for more precise localization of abnormalities. 3D mammography has the potential to improve outcomes by finding more cancers early while also reducing patient anxiety and healthcare costs from unnecessary additional testing.
Image processing techniques play a significant role in many areas in life, especially
in medical images, where they play a prominent role in diagnosing many diseases such
as detection of the brain tumor, breast cancer, kidney cancer, and the fractions.
Breast cancer is a common disease, regardless of the type of this disease, whether
it is benign or malignant, it is very dangerous and early detection may reduce the risk
of the disease spreading in the body leading to death. This work presents an approach
to detect breast cancer based on image processing algorithms, including image
preprocessing, enhancement, segmentation, Morphological operations, and feature
extraction to detect and extract the breast cancer region
This document summarizes a presentation on identifying microcalcifications in digital mammograms for early detection of breast cancer. It provides background on breast cancer and microcalcifications, outlines steps in computerized breast cancer detection systems including detection and diagnosis, and reviews literature on using techniques like wavelet and contourlet transforms to enhance mammograms and identify microcalcifications for improved cancer screening. The presentation will focus on microcalcification detection and diagnosis using a contourlet transform approach to enhance mammograms by applying directional filters to contourlet subbands before reconstructing an approximation of the mammogram with enhanced microcalcifications.
This document provides a comprehensive review and analysis of mammogram enhancement and segmentation techniques. It categorizes mammogram enhancement methods into four groups: conventional, region-based, feature-based, and fuzzy enhancement. Region-based and feature-based techniques can be used to enhance masses, while feature-based and fuzzy methods can also enhance micro-calcifications. The document also categorizes mammogram segmentation into breast region segmentation and region of interest segmentation. Region of interest segmentation using a single view is further divided into unsupervised techniques like region-based, contour-based, and clustering segmentation. The document evaluates and compares various enhancement and segmentation algorithms and highlights the best available approaches.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
The document discusses computer assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. It begins with an introduction to breast cancer and computer aided detection and diagnosis systems. It then provides background on areas of interest including improvement of pictorial information and machine vision. Next, it discusses microcalcifications, mammography, and mammograms. The document reviews literature on various preprocessing, feature extraction, and detection techniques. It identifies challenges in microcalcification detection including their small size and variable clusters. Finally, it outlines the plan of action for the thesis including use of the mini-MIAS mammogram database and a range of techniques to remove pectoral muscle and x-ray labels.
Segmentation of thermograms breast cancer tarek-to-slid shareTarek Gaber
This document presents a new method for segmenting regions of interest (ROIs) in breast thermograms to detect breast abnormalities. The method uses features extracted from the ROIs, like statistical and texture features, and supports vector machines for classification. It was tested on a database of 149 patients, achieving 100% accuracy in detecting normal vs. abnormal breasts. The method provides an automatic and low-cost approach to segmenting thermograms for breast cancer detection.
3D mammography is a new technology that produces 3D images of breast tissue by combining multiple x-ray images taken from different angles. Clinical trials have found that 3D mammography increases cancer detection rates by 47% compared to standard 2D mammography. It also reduces unnecessary patient recalls by 28-40% by minimizing issues with dense breast tissue. The enhanced 3D images allow for more precise localization of abnormalities. 3D mammography has the potential to improve outcomes by finding more cancers early while also reducing patient anxiety and healthcare costs from unnecessary additional testing.
Image processing techniques play a significant role in many areas in life, especially
in medical images, where they play a prominent role in diagnosing many diseases such
as detection of the brain tumor, breast cancer, kidney cancer, and the fractions.
Breast cancer is a common disease, regardless of the type of this disease, whether
it is benign or malignant, it is very dangerous and early detection may reduce the risk
of the disease spreading in the body leading to death. This work presents an approach
to detect breast cancer based on image processing algorithms, including image
preprocessing, enhancement, segmentation, Morphological operations, and feature
extraction to detect and extract the breast cancer region
This document summarizes a presentation on identifying microcalcifications in digital mammograms for early detection of breast cancer. It provides background on breast cancer and microcalcifications, outlines steps in computerized breast cancer detection systems including detection and diagnosis, and reviews literature on using techniques like wavelet and contourlet transforms to enhance mammograms and identify microcalcifications for improved cancer screening. The presentation will focus on microcalcification detection and diagnosis using a contourlet transform approach to enhance mammograms by applying directional filters to contourlet subbands before reconstructing an approximation of the mammogram with enhanced microcalcifications.
This document provides a comprehensive review and analysis of mammogram enhancement and segmentation techniques. It categorizes mammogram enhancement methods into four groups: conventional, region-based, feature-based, and fuzzy enhancement. Region-based and feature-based techniques can be used to enhance masses, while feature-based and fuzzy methods can also enhance micro-calcifications. The document also categorizes mammogram segmentation into breast region segmentation and region of interest segmentation. Region of interest segmentation using a single view is further divided into unsupervised techniques like region-based, contour-based, and clustering segmentation. The document evaluates and compares various enhancement and segmentation algorithms and highlights the best available approaches.
Enahncement method to detect breast cancerPunit Karnani
This document summarizes and compares existing methods for enhancing and segmenting mammogram images to aid in the detection of breast abnormalities like masses and calcifications. It categorizes enhancement methods into conventional, region-based, feature-based, and fuzzy techniques. Conventional techniques like histogram equalization are used to enhance masses, while region-based methods enhance features based on surroundings. Feature-based techniques enhance in the wavelet domain and can be used for both masses and calcifications. The document aims to provide a comprehensive review and analysis of mammogram enhancement and segmentation algorithms to highlight the best approaches.
Automated Breast Tumour Detection in Ultrasound Images Using Support Vector M...dbpublications
Breast cancer is the second leading cause of death in women after heart diseases. A well-known
statement in cancer society is “Early detection means better chances of survival”. In the past few years
several techniques were developed to detect breast tumors in early stages. A proposed system is designed
for breast tumors detection using ultrasound images. Ultrasound is used because it is less expensive and
less invasive than X-rays used in mammography and computerized tomography. It can provide a second
opinion for a physician to detect breast tumors.
IRJET- A Survey on Categorization of Breast Cancer in Histopathological ImagesIRJET Journal
This document summarizes various methods for categorizing breast cancer in histopathological images. It discusses machine learning and image processing techniques that have been used to build computer-aided diagnosis (CAD) systems to help pathologists diagnose breast cancer more objectively and consistently. The document reviews different classification methods that have been proposed, including those using fuzzy logic, level set methods, convolutional neural networks, texture features and ensemble methods. It concludes that accurately classifying histopathological images remains challenging due to limited publicly available datasets and variability in tissue appearance, but that machine learning and advanced image analysis offer promising approaches to improve cancer detection and diagnosis.
This document presents a method for detecting melanoma skin cancer using image processing and machine learning techniques. Images of skin lesions are first segmented using active contour models. Features like color, size, shape and texture are then extracted from the segmented images. Texture is analyzed using local binary patterns (LBP). The extracted features are used to classify images as melanoma or non-melanoma using a support vector machine (SVM) classifier. The goal is to develop an automated system for early detection of melanoma to help reduce death rates from this dangerous form of skin cancer.
This document presents an overview of a thesis project on computer-assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. The project aims to develop a system that can automatically detect microcalcifications in mammogram images to assist radiologists. The system will use techniques like image segmentation, morphological operations, filtering, and feature extraction to preprocess mammogram images and identify microcalcification clusters. A mini-MIAS database containing 322 mammogram images will be used to test and evaluate the methodology. The document outlines the background, motivation, challenges, plan of action and materials/tools for the project.
A survey on enhancing mammogram image saradha arumugam academiaPunit Karnani
This document summarizes research on enhancing mammogram images to improve the detection of breast cancer. It discusses how mammogram images have low contrast and are noisy, making it difficult to identify microcalcifications that could indicate cancer. Various image enhancement techniques are reviewed that aim to improve contrast, reduce noise, and sharpen edges to make microcalcifications more visible. The techniques discussed include nonlinear unsharp masking, wavelet-based enhancement, adaptive contrast enhancement, and integrated wavelet decompositions. Evaluation of the techniques suggests they can improve cancer diagnosis by enhancing image details and increasing radiologist performance.
IRJET- Skin Cancer Detection using Digital Image ProcessingIRJET Journal
This document describes research on developing a system for detecting skin cancer through digital image processing. The system uses dermoscopic images of skin lesions that are preprocessed to remove noise. Texture features are then extracted from the images using Gray Level Co-occurrence Matrices and Gabor filtering. These features are input into a support vector machine for classification of images into cancerous or non-cancerous categories. The researchers achieved an accuracy of 77% and discuss potential improvements and applications of the system to help dermatologists detect melanoma and other skin cancers at early stages.
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
IRJET- Texture Feature Extraction for Classification of MelanomaIRJET Journal
This document discusses using texture feature extraction and a support vector machine classifier to classify skin images as malignant melanoma or benign. It proposes extracting gray-level co-occurrence matrix features from skin images to capture texture characteristics. These features would then be input to a support vector machine classifier trained to differentiate between melanoma and non-melanoma skin images. The goal is to develop an automated computer-aided system for early detection of malignant melanoma from digital skin images.
SkinCure: An Innovative Smart Phone Based Application to Assist in Melanoma E...sipij
Melanoma spreads through metastasis, and therefore it has been proven to be very fatal. Statistical
evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma.
Further investigations have shown that the survival rates in patients depend on the stage of the infection;
early detection and intervention of melanoma implicates higher chances of cure. Clinical diagnosis and
prognosis of melanoma is challenging since the processes are prone to misdiagnosis and inaccuracies due
to doctors’ subjectivity. This paper proposes an innovative and fully functional smart-phone based
application to assist in melanoma early detection and prevention. The application has two major
components; the first component is a real-time alert to help users prevent skin burn caused by sunlight; a
novel equation to compute the time for skin to burn is thereby introduced. The second component is an
automated image analysis module which contains image acquisition, hair detection and exclusion, lesion
segmentation, feature extraction, and classification. The proposed system exploits PH2 Dermoscopy image
database from Pedro Hispano Hospital for development and testing purposes. The image database
contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases.
The experimental results show that the proposed system is efficient, achieving classification of the normal,
atypical and melanoma images with accuracy of 96.3%, 95.7% and 97.5%, respectively.
IRJET- Detection and Classification of Breast Cancer from Mammogram ImageIRJET Journal
This document discusses techniques for detecting and classifying breast cancer from mammogram images. It begins with an introduction to breast cancer and the importance of early detection using mammography. The proposed system utilizes the MIAS mammogram dataset and performs preprocessing, segmentation, feature extraction using texture analysis, and classification using multilayer perceptron. K-fold cross validation is used to evaluate the model. The goal is to develop an automated system for mammogram analysis to improve early detection of breast cancer.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
IRJET - Histogram Analysis for Melanoma Discrimination in Real Time ImageIRJET Journal
The document presents a novel framework for recognizing melanoma in real-time skin images using k-means clustering and support vector machine algorithms. It discusses the challenges of automated melanoma recognition due to variations in melanoma appearance and similarities to non-melanoma lesions. A two-stage approach is proposed involving lesion segmentation followed by classification using deep learning networks to extract discriminative features for accurate melanoma recognition.
This document summarizes recent developments in digital analysis techniques for breast imaging, including mammography, MRI, and computer-assisted diagnosis systems. It discusses how digital mammography and computer algorithms can help detect cancers and reduce false positives compared to analog mammography. It also describes how MRI provides additional functional information and computer-assisted diagnosis can help standardize assessments of breast density and lesion characteristics. Emerging techniques like radiomics and deep learning extract large amounts of imaging features and use machine learning to correlate imaging phenotypes with cancer outcomes or risk factors.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
This paper explains new imaging techniques that show promising results in breast cancer detection. The
presented techniques use microwave-based methods, wavelet analyses, and neural networks to get a
suitable resolution for the breast image. One of the presented techniques (hybrid method) uses a
combination of microwaves and acoustic signals to improve the detection capability. Some promising
results are shown and explained.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Progressive Review on Early Stage Breast Cancer DetectionIRJET Journal
This document provides a progressive review of techniques for early stage breast cancer detection. It discusses various image segmentation methods used in previous research like threshold-based, region-based, edge-based, and clustering-based segmentation. Feature extraction techniques discussed include gray level co-occurrence matrix, principal component analysis, and linear discriminant analysis. Classifiers reviewed are convolutional neural networks, support vector machines, artificial neural networks, random forests, and decision trees. The paper analyzes the merits and limitations of these techniques and identifies convolutional neural networks and artificial neural networks as providing high accuracy for breast cancer classification from images.
A Progressive Review: Early Stage Breast Cancer Detection using Ultrasound Im...IRJET Journal
1) The document reviews various machine learning algorithms and imaging modalities for early-stage breast cancer detection.
2) It discusses studies that have used ultrasound imaging alone or in combination with mammography to detect breast cancers at early stages.
3) Machine learning algorithms like convolutional neural networks have shown promise in classifying ultrasound images to detect breast cancer when trained on large datasets.
Enahncement method to detect breast cancerPunit Karnani
This document summarizes and compares existing methods for enhancing and segmenting mammogram images to aid in the detection of breast abnormalities like masses and calcifications. It categorizes enhancement methods into conventional, region-based, feature-based, and fuzzy techniques. Conventional techniques like histogram equalization are used to enhance masses, while region-based methods enhance features based on surroundings. Feature-based techniques enhance in the wavelet domain and can be used for both masses and calcifications. The document aims to provide a comprehensive review and analysis of mammogram enhancement and segmentation algorithms to highlight the best approaches.
Automated Breast Tumour Detection in Ultrasound Images Using Support Vector M...dbpublications
Breast cancer is the second leading cause of death in women after heart diseases. A well-known
statement in cancer society is “Early detection means better chances of survival”. In the past few years
several techniques were developed to detect breast tumors in early stages. A proposed system is designed
for breast tumors detection using ultrasound images. Ultrasound is used because it is less expensive and
less invasive than X-rays used in mammography and computerized tomography. It can provide a second
opinion for a physician to detect breast tumors.
IRJET- A Survey on Categorization of Breast Cancer in Histopathological ImagesIRJET Journal
This document summarizes various methods for categorizing breast cancer in histopathological images. It discusses machine learning and image processing techniques that have been used to build computer-aided diagnosis (CAD) systems to help pathologists diagnose breast cancer more objectively and consistently. The document reviews different classification methods that have been proposed, including those using fuzzy logic, level set methods, convolutional neural networks, texture features and ensemble methods. It concludes that accurately classifying histopathological images remains challenging due to limited publicly available datasets and variability in tissue appearance, but that machine learning and advanced image analysis offer promising approaches to improve cancer detection and diagnosis.
This document presents a method for detecting melanoma skin cancer using image processing and machine learning techniques. Images of skin lesions are first segmented using active contour models. Features like color, size, shape and texture are then extracted from the segmented images. Texture is analyzed using local binary patterns (LBP). The extracted features are used to classify images as melanoma or non-melanoma using a support vector machine (SVM) classifier. The goal is to develop an automated system for early detection of melanoma to help reduce death rates from this dangerous form of skin cancer.
This document presents an overview of a thesis project on computer-assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. The project aims to develop a system that can automatically detect microcalcifications in mammogram images to assist radiologists. The system will use techniques like image segmentation, morphological operations, filtering, and feature extraction to preprocess mammogram images and identify microcalcification clusters. A mini-MIAS database containing 322 mammogram images will be used to test and evaluate the methodology. The document outlines the background, motivation, challenges, plan of action and materials/tools for the project.
A survey on enhancing mammogram image saradha arumugam academiaPunit Karnani
This document summarizes research on enhancing mammogram images to improve the detection of breast cancer. It discusses how mammogram images have low contrast and are noisy, making it difficult to identify microcalcifications that could indicate cancer. Various image enhancement techniques are reviewed that aim to improve contrast, reduce noise, and sharpen edges to make microcalcifications more visible. The techniques discussed include nonlinear unsharp masking, wavelet-based enhancement, adaptive contrast enhancement, and integrated wavelet decompositions. Evaluation of the techniques suggests they can improve cancer diagnosis by enhancing image details and increasing radiologist performance.
IRJET- Skin Cancer Detection using Digital Image ProcessingIRJET Journal
This document describes research on developing a system for detecting skin cancer through digital image processing. The system uses dermoscopic images of skin lesions that are preprocessed to remove noise. Texture features are then extracted from the images using Gray Level Co-occurrence Matrices and Gabor filtering. These features are input into a support vector machine for classification of images into cancerous or non-cancerous categories. The researchers achieved an accuracy of 77% and discuss potential improvements and applications of the system to help dermatologists detect melanoma and other skin cancers at early stages.
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
IRJET- Texture Feature Extraction for Classification of MelanomaIRJET Journal
This document discusses using texture feature extraction and a support vector machine classifier to classify skin images as malignant melanoma or benign. It proposes extracting gray-level co-occurrence matrix features from skin images to capture texture characteristics. These features would then be input to a support vector machine classifier trained to differentiate between melanoma and non-melanoma skin images. The goal is to develop an automated computer-aided system for early detection of malignant melanoma from digital skin images.
SkinCure: An Innovative Smart Phone Based Application to Assist in Melanoma E...sipij
Melanoma spreads through metastasis, and therefore it has been proven to be very fatal. Statistical
evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma.
Further investigations have shown that the survival rates in patients depend on the stage of the infection;
early detection and intervention of melanoma implicates higher chances of cure. Clinical diagnosis and
prognosis of melanoma is challenging since the processes are prone to misdiagnosis and inaccuracies due
to doctors’ subjectivity. This paper proposes an innovative and fully functional smart-phone based
application to assist in melanoma early detection and prevention. The application has two major
components; the first component is a real-time alert to help users prevent skin burn caused by sunlight; a
novel equation to compute the time for skin to burn is thereby introduced. The second component is an
automated image analysis module which contains image acquisition, hair detection and exclusion, lesion
segmentation, feature extraction, and classification. The proposed system exploits PH2 Dermoscopy image
database from Pedro Hispano Hospital for development and testing purposes. The image database
contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases.
The experimental results show that the proposed system is efficient, achieving classification of the normal,
atypical and melanoma images with accuracy of 96.3%, 95.7% and 97.5%, respectively.
IRJET- Detection and Classification of Breast Cancer from Mammogram ImageIRJET Journal
This document discusses techniques for detecting and classifying breast cancer from mammogram images. It begins with an introduction to breast cancer and the importance of early detection using mammography. The proposed system utilizes the MIAS mammogram dataset and performs preprocessing, segmentation, feature extraction using texture analysis, and classification using multilayer perceptron. K-fold cross validation is used to evaluate the model. The goal is to develop an automated system for mammogram analysis to improve early detection of breast cancer.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
IRJET - Histogram Analysis for Melanoma Discrimination in Real Time ImageIRJET Journal
The document presents a novel framework for recognizing melanoma in real-time skin images using k-means clustering and support vector machine algorithms. It discusses the challenges of automated melanoma recognition due to variations in melanoma appearance and similarities to non-melanoma lesions. A two-stage approach is proposed involving lesion segmentation followed by classification using deep learning networks to extract discriminative features for accurate melanoma recognition.
This document summarizes recent developments in digital analysis techniques for breast imaging, including mammography, MRI, and computer-assisted diagnosis systems. It discusses how digital mammography and computer algorithms can help detect cancers and reduce false positives compared to analog mammography. It also describes how MRI provides additional functional information and computer-assisted diagnosis can help standardize assessments of breast density and lesion characteristics. Emerging techniques like radiomics and deep learning extract large amounts of imaging features and use machine learning to correlate imaging phenotypes with cancer outcomes or risk factors.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
This paper explains new imaging techniques that show promising results in breast cancer detection. The
presented techniques use microwave-based methods, wavelet analyses, and neural networks to get a
suitable resolution for the breast image. One of the presented techniques (hybrid method) uses a
combination of microwaves and acoustic signals to improve the detection capability. Some promising
results are shown and explained.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Progressive Review on Early Stage Breast Cancer DetectionIRJET Journal
This document provides a progressive review of techniques for early stage breast cancer detection. It discusses various image segmentation methods used in previous research like threshold-based, region-based, edge-based, and clustering-based segmentation. Feature extraction techniques discussed include gray level co-occurrence matrix, principal component analysis, and linear discriminant analysis. Classifiers reviewed are convolutional neural networks, support vector machines, artificial neural networks, random forests, and decision trees. The paper analyzes the merits and limitations of these techniques and identifies convolutional neural networks and artificial neural networks as providing high accuracy for breast cancer classification from images.
A Progressive Review: Early Stage Breast Cancer Detection using Ultrasound Im...IRJET Journal
1) The document reviews various machine learning algorithms and imaging modalities for early-stage breast cancer detection.
2) It discusses studies that have used ultrasound imaging alone or in combination with mammography to detect breast cancers at early stages.
3) Machine learning algorithms like convolutional neural networks have shown promise in classifying ultrasound images to detect breast cancer when trained on large datasets.
Breast Cancer Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict breast cancer from patient data and imaging results. It first provides background on breast cancer, noting it is the most commonly diagnosed cancer worldwide. The document then reviews prior works applying machine learning to breast cancer prediction, finding support vector machines achieved the highest accuracy. It describes the dataset used, from the University of Wisconsin, containing patient data and tumor characteristics. Finally, it explores the data and discusses implementing classification algorithms like logistic regression, support vector machines, random forests and neural networks to predict cancer type, finding logistic regression achieved the highest accuracy of 98.24%.
Breast Cancer Detection through Deep Learning: A ReviewIRJET Journal
Three sentences:
Convolutional neural networks show promise for automating breast cancer detection from medical images to address increasing caseloads. Previous research has developed CNN approaches for segmenting breast tissue and detecting cancer from mammograms and ultrasound images with strong accuracy. This literature review evaluates past works applying deep learning to breast cancer detection and segmentation tasks to inform the development of a new CNN-based approach detailed in future work.
A Comparative Study on the Methods Used for the Detection of Breast Cancerrahulmonikasharma
Among women in the world, the death caused by the Breast cancer has become the leading role. At an initial stage, the tumor in the breast is hard to detect. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan which adopts X-ray method with high resolution film. so that it can detect well the tumors in the breast. This paper describes the comparative study of the various data mining methods on the detection of the breast cancer by using image processing techniques.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
The Evolution and Impact of Medical Science Journals in Advancing Healthcaresana473753
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
"journals" refer to academic or professional publications that contain articles and research papers related to various aspects of the medical field. These journals serve as a platform for the dissemination of new medical knowledge, research findings, clinical studies, and expert opinions. They play a crucial role in advancing medical science, sharing best practices, and keeping healthcare professionals, researchers, and students informed about the latest developments in medicine and related disciplines.
A New Approach to the Detection of Mammogram Boundary IJECEIAES
Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
Logistic Regression Model for Predicting the Malignancy of Breast CancerIRJET Journal
1. The document describes using a logistic regression machine learning model to predict whether breast cancer is benign or malignant based on characteristics of breast cell samples.
2. The model was trained on a dataset of 569 samples described by 33 characteristics and achieved 94.94% accuracy on the training data and 92.10% accuracy on the test data.
3. The model provides a way to efficiently diagnose breast cancer and determine the appropriate treatment needed based on whether the cancer is predicted as benign or malignant.
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.
SKIN CANCER DETECTION AND SEVERITY PREDICTION USING DEEP LEARNINGIRJET Journal
This document discusses using deep learning techniques to detect skin cancer and predict its severity. It first describes the types of skin cancer (melanoma, squamous cell carcinoma, basal cell carcinoma) and importance of early detection. It then reviews previous literature on skin cancer detection using methods like deep neural networks and support vector machines. The paper proposes detecting cancer types (melanoma, squamous, basal cell) using two approaches refined on two skin condition datasets. It aims to identify skin diseases across domains for more accurate detection and severity prediction of skin cancer.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Classification AlgorithmBased Analysis of Breast Cancer DataIIRindia
The classification algorithms are very frequently used algorithms for analyzing various kinds of data available in different repositories which have real world applications. The main objective of this research work is to find the performance of classification algorithms in analyzing Breast Cancer data via analyzing the mammogram images based its characteristics.Different attribute values of cancer affected mammogram images are considered for analysis in this work. The Patients food habits, age of the patients, their life styles, occupation, their problem about the diseases and other information are taken into account for classification. Finally, performance of classification algorithms J48, CART and ADTree are given with its accuracy. The accuracy of taken algorithms is measured by various measures like specificity, sensitivity and kappa statistics (Errors).
A Comprehensive Study on the Phases and Techniques of Breast Cancer Classific...IRJET Journal
This document provides a comprehensive summary of techniques for breast cancer classification. It discusses three main phases: pre-processing, segmentation, and classification. For pre-processing, techniques like noise suppression, background removal, and edge detection are used. Segmentation involves separating the area of concern from breast tissue. Classification classifies tumors as benign or malignant. The document reviews various algorithms for each phase, finding techniques like Canny edge detection and Hough's line transform effective for segmentation. It also discusses challenges like limited radiologist resources and potential for human error in diagnosis.
Automated breast cancer detection system from breast mammogram using deep neu...nooriasukmaningtyas
All over the world breast cancer is a major disease which mostly affects the women and it may also cause death if it is not diagnosed in its early stage. But nowadays, several screening methods like magnetic resonance imaging (MRI), ultrasound imaging, thermography and mammography are available to detect the breast cancer. In this article mammography images are used to detect the breast cancer. In mammography image the cancerous lumps/microcalcifications are seen to be tiny with low contrast therefore it is difficult for the doctors/radiologist to detect it. Hence, to help the doctors/radiologist a novel system based on deep neural network is introduced in this article that detects the cancerous lumps/microcalcifications automatically from the mammogram images. The system acquires the mammographic images from the mammographic image analysis society (MIAS) data set. After pre-processing these images by 2D median image filter, cancerous features are extracted from the images by the hybridization of convolutional neural network with rat swarm optimization algorithm. Finally, the breast cancer patients are classified by integrating random forest with arithmetic optimization algorithm. This system identifies the breast cancer patients accurately and its performance is relatively high compared to other approaches.
The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.
A Comprehensive Evaluation of Machine Learning Approaches for Breast Cancer C...IRJET Journal
This document compares the performance of three machine learning models - Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) - for classifying breast cancer from histopathology images. CNN achieved the highest classification accuracy of 87%, followed by SVM at 76% and ANN at 71%. CNN also exhibited superior sensitivity in detecting malignant cases. The document proposes using CNN, ANN, and SVM models on a breast cancer histopathology image dataset to determine which model provides the most accurate cancer classification.
Detection of Skin Cancer Based on Skin Lesion Images UsingDeep LearningIRJET Journal
This document discusses skin cancer detection using deep learning techniques. It begins with an introduction to skin cancer and the need for early detection. It then reviews the existing methods for skin cancer detection which rely on visual examination by dermatologists. The proposed method uses a deep learning model trained on skin lesion images to classify lesions as benign or malignant. The methodology section describes the image acquisition, preprocessing including enhancement, data augmentation, and preparation steps. It then discusses training a convolutional neural network for classification. Experimental results show the system can accurately detect different types of skin cancers like basal cell carcinoma and keratosis. The conclusion discusses benefits of developing such a system for integrated use on smartphones to enable low-cost cancer screening.
Similar to IRJET- Comparison of Breast Cancer Detection using Probabilistic Neural Network and Support Vector Machine (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.