Classifying Brain Tumors from MRI Images Using Object Detection and CNN

Abstract

This paper investigates the crucial field of magnetic resonance imaging (MRI) and convolution neural networks (CNN) for brain tumor identification. A brain tumor is an aberrant cell proliferation that disrupts normal brain function and may pose serious health risks. Leveraging the power of CNN, our approach seeks to revolutionize the identification process through the analysis of MRI images. The CNN acts as a sophisticated interpreter, meticulously scrutinizing intricate patterns within brain scans to discern subtle abnormalities indicative of tumors. By training the CNN on diverse MRI datasets, our method enhances accuracy in distinguishing between healthy and pathological neural structures. This research signifies a pivotal advancement in medical diagnostics, showcasing how CNN-driven analysis of MRI images contributes to early and precise detection of brain tumors, offering improved prospects for patient care and treatment outcomes