Brain Tumor Detection and Classification Using MRI with ResNet50 and Hybrid AI Deep Learning Techniques

This paper tackles the critical challenge of detecting brain tumors using MRI images, a vital task in medical diagnostics that requires both high accuracy and clear interpretability. While deep learning has achieved significant milestones in medical image analysis, there remains a pressing need for approaches that combine precision with transparency for medical practitioners. Deep convolutional neural networks (CNNs) are often perceived as black-box models, offering limited insights into their decision-making processes. To address this issue, the study proposes a method leveraging the enhanced ResNet50 deep learning model to create a comprehensible framework for brain tumor diagnosis. Utilizing an enhanced MRI image dataset for optimized model training, the approach achieved an impressive testing accuracy of 98.89%, with recall, precision, and related metrics exceeding 99%. These exceptional results demonstrate the model's effectiveness in accurately identifying malignancies. The enhanced interpretability and superior accuracy present significant potential for advancing diagnostic tools, paving the way for more transparent and reliable methods in brain tumor detection.