JMIR Publications

ABSTRACT

Brain tumor is one of the most malignant diseases of the central nervous system, and early accurate detection is of great significance for improving patient survival rate. However, the heterogeneity of brain tumors in terms of morphology, size, and location on MRI image, as well as their similarity to surrounding normal brain tissue, poses significant challenges for tumor detection. This paper, based on YOLOv11, proposes a Convolution-Prewitt and Pooling-based Preprocessing (CSPP) which highlights important structural detail more effectively than traditional statistics. A Dynamic Convolution-based C3k2 (DCC) is integrated to more efficiently capture both local and global features. A Channel Prior Convolutional Attention (CPCA) is introduced before the detection head, enabling the network to specifically focus on information-rich channels and key spatial regions. Through Progressive Hybrid Pruning Strategy (PHPS) , the model is lightweighted for efficient inference. Furthermore, Eigen-CAM is utilized to interpret the prediction result, making them more transparent and clinically valuable. On Br35H, mAP50 increased by 2.6%, mAP50:95 increased by 5.9%, and GFLOPs were reduced by 47.7%. On YOLO, mAP50 increased by 19.5%, mAP50:95 increased by 7.7%, and GFLOPs were reduced by 47.7%. On Capstone, mAP50 increased by 6.9%, mAP50:95 increased by 5.8%, and GFLOPs were reduced by 47.7%.