Keywords
Brain Tumor Detection
Deep Learning
C2f-DCNv2 Module
DySample Upsampling Operator
How to Cite
Abstract
Early detection and accurate diagnosis of brain tumors are crucial for patient treatment and prognosis. Although traditional medical image analysis methods have made certain progress, they still face challenges such as relying on manual experience and insufficient accuracy. To address this, this paper proposes a brain tumor detection method based on deep learning, which combines an improved YOLOv8n network, an innovative C2f-DCNv2 module, and the DySample upsampling operator. By introducing the C2f-DCNv2 module, the model integrates deformable convolutions and multi-branch structures in the convolutional neural network (CNN), effectively enhancing the ability to extract tumor features. The DySample upsampling operator dynamically adjusts the sampling grid, significantly improving image detail restoration. In experiments, the proposed model achieved significant improvements over YOLOv8n. The recall rate increased from 86.5% in YOLOv8n to 88.2% in the proposed model, a 1.7% improvement. The mAP@0.5 (mean average precision) reached 93.0% in the proposed model, compared to 92.2% in YOLOv8n, a 0.8% improvement. The mAP@0.5-0.95 reached 66.5%, compared to YOLOv8n's 64.9%, an improvement of 1.6%. These results demonstrate that the proposed method outperforms existing YOLO series models in multiple key metrics, offering stronger detection accuracy and robustness. The method provides an efficient and accurate solution for automatic brain tumor detection and lays a foundation for future applications of deep learning in medical image analysis.
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