Brain Tumor Analysis using SVM and Score function

JISNEY THOMAS; MIDHU  YESODH; PRINCY P

CALL FOR PAPERS : Feb-2026

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25-Feb-26

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Brain Tumor Analysis using SVM and Score function

Author(s):

JISNEY THOMAS , THEJUS ENGINEERING COLLEGE ,VELLARAKAD,THRISSUR,KERALA; MIDHU YESODH, THEJUS ENGINEERING COLLEGE ,VELLARAKAD,THRISSUR,KERALA; PRINCY P, THEJUS ENGINEERING COLLEGE ,VELLARAKAD,THRISSUR,KERALA

Keywords:

Content Based Image Retrieval (CBIR); Discrete Wavelet Transform (DWT); Euclidean Distance; Support Vector Machine (SVM)

Abstract:

Medical field is very much depended on image processing nowadays. Brain tumor is very dangerous and harmful type of cancer. But diagnosis and treatment of brain tumour cost is very high and it lasts for longer period. The number of neuro patients is increasing, which in turn increases burden on small group of radiologists. So we need more efficient Tumour diagnosis system that help the Radiologists. In this project, a new CBIR method is introduced to detect tumour in tumorous image. Every CBIR system has feature extraction and classification. Here feature is extracted using Discrete Wavelet Transform and images are classified using Support Vector Machine .So when we give a Brain MRI image, the image is classified as normal or tumorous image. If the image is tumorous, then change detection method that searches for the most dissimilar region (axis-parallel bounding boxes) between the left and the right halves of a brain in an axial view MR slice. This change detection process uses a novel score function based on Bhattacharya coefficient computed with gray level intensity histograms.