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.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
For the scope of this project, it was decided to analyze the data to form distinct clusters based on their tumor type. Unsupervised learning (K-means clustering and hierarchical clustering) were used. Also, it was decided to analyze this data as a classification task. Based on different attributes (primarily mass spectrometry analysis results for 12553 proteins) few classification algorithms were implemented to see if the model can generate the accurate label of cancer type.
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
For the scope of this project, it was decided to analyze the data to form distinct clusters based on their tumor type. Unsupervised learning (K-means clustering and hierarchical clustering) were used. Also, it was decided to analyze this data as a classification task. Based on different attributes (primarily mass spectrometry analysis results for 12553 proteins) few classification algorithms were implemented to see if the model can generate the accurate label of cancer type.
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
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
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
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.
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
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
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.
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
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
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.
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
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
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.
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.
Early Detection of Lung Cancer Using Neural Network TechniquesIJERA Editor
Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM) format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II) and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall), precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.
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.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
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
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.