Medical professionals rely on MRI scans to evaluate brain tumors, a task prone to fatigue and subjective interpretation. This study enhances the YOLOv10 model by integrating two innovative modules: AvConv module and Bidirectional Pathway, both designed to improve detection accuracy. Experiments were conducted using two Kaggle datasets (Dataset 1: 1116 images; Dataset 2: 153 images), both annotated for tumor presence. The proposed model was compared against eight mainstream object detection methods. Results demonstrate that the proposed AvConv module achieves mAP50 of 88.5% and mAP50-95 of 77.7%, which represent relative improvements of 0.3% and 3.1%, respectively, over the baseline YOLOv10 achieves mAP50 of 88.2% and mAP50-95 of 74.6%. Meanwhile, the Bi-directional detection model reaches 90.3% mAP50 and 83.5% mAP50-95, corresponding to 2.1% and 8.9% gains relative to YOLOv10. These enhancements validate the effectiveness of the introduced modules in advancing detection accuracy while maintaining competitive inference efficiency, supporting the role of customized deep learning architectures in reliable, automated brain tumor diagnosis.
Guo et al. (Sat,) studied this question.