Brain Tumor Detection from MRI Images Using YOLOv8 Deep Learning Model

This study proposed an efficient deep learning based approach for brain tumor detection using the YOLOv8 architecture.MRI scans are utilized to train and evaluate the model on a publicly available dataset.The proposed YOLOv8 framework effectively identifies and localizes tumor regions by leveraging enhanced convolutional blocks, multi-scale feature fusion and anchor free detection mechanisms.The model achieved a mean average precision (mAP@0.5) of 0.94, precision of 0.95, recall of 0.94 and F1-score of 0.94 and outperformed several existing YOLO-based approaches.The results demonstrate YOLOv8 robustness in accurate detection of tumors with high inference speed and it is suitable for real-time diagnostic applications.Overall, the proposed method shows great potential for computer-aided diagnosis and support for clinicians in brain tumor assessment.