Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/546191
Title: An optimized support vector machine and deep convolutional neural network model to enhance brain tumor segmentation on magnetic resonance brain images
Researcher: Krishnakumar, S
Guide(s): Benadictraja, J
Keywords: Computer Science
Computer Science Information Systems
Engineering and Technology
University: Anna University
Completed Date: 2024
Abstract: Meningioma brain tumors are crucial and life killing disease among newlinethe other types of brain tumors. Hence, there timely and accurate detection and newlinesegmentation of tumor regions are very important to save the human life. In this newlinethesis, three different approaches are proposed to detect and classify the newlinemeningioma brain images from the non-meningioma brain images. newlineIn approach-1, rough k means clustering algorithm and Multi Kernel newlineSupport Vector Machine (MKSVM) algorithm is proposed to detect the tumor newlineregions in meningioma brain images. The purpose of these methods are to newlineprovide a Magnetic Resonance (MR) image segmentation, to raise accuracy for newlineclassification of the tumor feature direction and also maximizes and classified a newlineMR image. The preprocessing method is applied in this work to improve the newlineaccuracy of image segmentation and to reduce the noise. There are three steps newlineare followed in this work to achieve a effective results such as (i) The input of newlinebrain MRI images are preprocessed. (ii) The preprocessed images are delivered newlineto the feature extraction process then the feature extraction process is performed newlineby Improved Gabor Wavelet Transform (IGWT) (iii) Finally, feature values are newlinetransferred in to the clustering process for segmentation process. newline
Pagination: xvii110
URI: http://hdl.handle.net/10603/546191
Appears in Departments:Faculty of Information and Communication Engineering

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02_prelim pages.pdf7.74 MBAdobe PDFView/Open
03_content.pdf52.69 kBAdobe PDFView/Open
04_abstract.pdf50.11 kBAdobe PDFView/Open
05_chapter1.pdf545.5 kBAdobe PDFView/Open
06_chapter2.pdf183.4 kBAdobe PDFView/Open
07_chapter3.pdf852.99 kBAdobe PDFView/Open
08_chapter4.pdf1.04 MBAdobe PDFView/Open
09_chapter5.pdf1.09 MBAdobe PDFView/Open
10_annexures.pdf119.1 kBAdobe PDFView/Open
80_recommendation.pdf108.91 kBAdobe PDFView/Open
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