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Home / programming language / Online Matlab Projects / Automatic Brain Hemorrhage Segmentation and Classification in CTscan Images
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Matlab Projects - Digital Image Proecessing, Communication, Academic Projects.
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Automatic Brain Hemorrhage Segmentation and Classification in CTscan Images

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SKU: PROJ7659 Categories: 2017 Projects, Digital Image Processing Projects, Final Year Projects, Online Matlab Projects, programming language Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Automatic Brain Hemorrhage Segmentation and Classification in CTscan Images

Abstract— Brain hemorrhage detection and classification is a major help to physicians to rescue patients in an early stage. We have tried to introduce an automatic detection and classification method to improve and accelerate the process of physicians’ decision-making. To achieve this purpose, at first we have used a simple and effective segmentation method to detect and separate the hemorrhage regions from other parts of the brain, and then we have extracted a number of features from each detected hemorrhage region. We selected some of convenient features by using a Genetic Algorithm (GA)-based feature selection algorithm. Eventually, we have classified the different types of hemorrhages. Our algorithm is evaluated on a perfect set of CT-scan images and the segmentation accuracy for three major types of hemorrhages (EDH, ICH and SDH) obtained 96.22%, 95.14% and 90.04%, respectively. In the classification step, multilayer neural network could be more successful than the KNN classifier because of its higher accuracy (93.3%). Finally, we achieved the accuracy rate of more than 90% for the detection and classification of brain hemorrhages. < final year projects >

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