Title: Brain Tumor Detection from MRI Images Using YOLOv8 Deep Learning Model


Authors: Vinodkumar R. Patil, Archana S. Vaidya, Manisha S. Patil


Published in: Volume 3 Issue 1 Jan June 2026, Page No. 142-146


DOI: 10.63844/IJAITR.v3.i1.2026.142-146 cite


Keywords: Tumor, YOLOv8, MRI, Feature, Detection


Abstract: 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.


Download PDF