Machine Learning Approaches to Brain MRI Analysis and Automated Medical Reporting System

Brain tumors represent a serious medical condition that requires timely and precise examination to ensure effective treatment. This paper focuses on evaluating all of the latest convolutional neural network (CNN) architectures for brain tumor detection and classification from MRI/CT scans. Using architectures such as VGG16, ResNet50, EfficientNet, and YOLOv8, this paper seeks to determine which of these architectures produces the strongest overall performance and assists in the identification of the presence of contemporary tumors and classification of their type. This complete study aims to enhance diagnostic accuracy, accessibility, and efficiency in medical practice contributing to higher accuracy rates.