Web Application to Detect Brain Tumour Using MRI

Abstract

A serious neurological illness known as a brain tumour is one in which the brain or skull's cells proliferate out of control. As the mortality rates for this condition continue to climb, manual examination of Magnetic Resonance Images (MRIs) is insufficient for accurate diagnosis, and early discovery is essential for patient survival. Employing ResNet-50 as a tool for early detection, this project focuses on improving the accuracy of brain tumour diagnosis. The proposed ResNet-50 is trained on a combined dataset from three sources: Figshare, SARTAJ, and Br35H, which contains 7023 Magnetic Resonance Images (MRI) scans belonging to four categories: Glioma, Meningioma, No-tumour, and Pituitary. The model is trained using several pre-processing strategies, resulting in a proposed ResNet-50 based Computer-Aided Diagnosis (CAD) system that achieved an accuracy of 98.70%. The model is deployed using Flask, connecting its Application Programming Interface (API) to the front-end. The user-friendly interface, designed with Tailwind CSS and Next.js, enables seamless interaction with the system and utilizing efficient brain tumour detection capabilities. The techniques developed in this project have the capability to assist clinicians specialising in the timely identification of brain tumours. Keywords: Brain tumour detection, Deep learning, Magnetic resonance imaging (MRI), ResNet-50, Computer-aided diagnosis (CAD), Flask, Next.js