hybridized machine learning algorithm for brain pathology classification
E.Edith Esther,Sudharsan J,Santhosh S M,Dhanush N
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 22 April,2024 Pages:1852-1858
Abstract
The most common medical problems in brain tumor patients include the management of glioma, meningioma, non-tumor condition, pituitary tumor and cognitive dysfunction. Despite their importance, there are relatively few studies specifically addressing these issues. There is increasing evidence that brain tumor patients who have not had a seizure do not benefit from prophylactic antiepileptic medications. In the realm of medical diagnostics, the accurate and timely identification of brain tumors plays a pivotal role in patient care and treatment planning. This project endeavors to harness the power of machine learning, specifically through the implementation of a Convolutional Neural Network (CNN) utilizing the MobileViT (Mobile Vision Transformer) architecture, to facilitate the precise classification of brain tumor MRI images into four distinct categories. By amalgamating cutting-edge machine learning techniques with medical imaging, this research aims to enhance diagnostic accuracy, reduce human error, and expedite the decision-making process.
Kewords
Brain pathology, Tumor diagnosis, Image processing, Vision Transformer Algorithm
Reference
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