Brain Tumor Prediction using Adaptive Connected Component based GLCM and SVM Method

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Published: Aug 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9s.7459
Keywords:
Brain tumor, Support Vector Machine, Grey Level Co-occurrence Matrix, Clssification, Benign, Malignant

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A. Srinivasa Reddy
Associate Professor, Department of CSIT, CVR College of Engineering, Hyderabad, India
R. Raja
Associate Professor, Department of CSE (Cyber Security), CVR College of Engineering, Hyderabad, India
N. Satheesh
Professor, Department of CSE (AI), SET, Jain Global Campus, Jain University, Bangalore, India
R. Muruganantham
Professor, Department of IT, TKR College of Engineering and Technology, Hyderabad, India

Abstract

A crucial stage in the diagnosis of brain disorders using magnetic resonance images is feature extraction. The feature extraction procedure is used to reduce the amount of the picture data by removing the necessary information from the segmented image. The segmentation strategy and features that are extracted have an impact on the classification algorithm's dependability. With the aid of a Support Vector Machine, texture features are retrieved in this study using a Grey Level Co-occurrence Matrix, while form features are extracted using connected areas. Images of benign tumours, malignant tumours, and a normal brain all exhibit distinctive features. The classification of MR images can benefit from this change in feature values. A SVM classifier will receive the features that were thusly obtained for training and testing and further able to classify the abnormalities in brain images.

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How to Cite
Reddy, A. S. ., Raja, R. ., Satheesh, N. ., & Muruganantham, R. . (2023). Brain Tumor Prediction using Adaptive Connected Component based GLCM and SVM Method. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 485–491. https://doi.org/10.17762/ijritcc.v11i9s.7459
Issue
Vol. 11 No. 9s (2023)
Section
Articles

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