Brain Tumor Detection and Segmentation MRI Images Using Deep Learning

Abstract

Because of the intricate nature of tumor formations and their neurological effects, brain tumor identification and segmentation continue to be significant issues in medical imaging. With an AlexNet-based CNN for classification and YOLOv8 for segmentation, this study offers a sophisticated deep learning architecture that achieves 96% classification accuracy and 93.8% mAP@0.5 for segmentation. Our system, which was trained on 5,064 annotated MRI scans from Kaggle, performs better than earlier iterations of YOLO and is especially strong at detecting meningiomas (98.5% mAP). The methodology provides a scalable solution for clinical procedures by drastically cutting down on analysis time without sacrificing diagnostic accuracy. This study highlights how automated, trustworthy tumor analysis using AI-driven diagnostics can improve healthcare outcomes.