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Deep learning-based image processing methods for medical brain tumors are current research hotspots in this field. However, a great deal of research has focused on how to classify and segment brain tumors, while relatively little research has been done on brain tumor detection. This is mainly because brain tumor images are often filled with indistinguishable lesions, which can easily lead to false positives or missed diagnoses, and the YOLO detection algorithm has attracted a lot of attention due to its efficient real-time target detection capability. Based on the YOLO framework, we created a new neural network to accurately identify lesion regions in brain tumor medical images.The core of the approach is to propose a feature extraction network based on reparameterized heterogeneous convolution of large kernels(RGNet), and a incorporating an attention grather and distribute strategy(GDB). RGNet can better cope with significant changes in scale and different contextual feature textures in brain tumor detection, and GDB aggregates high-level and low-level semantic features and spatial details to overcome the information loss problem of the traditional feature pyramid module during feature fusion. information loss problem, and utilizes structured convolution module and channel mixing technique to improve the refinement of multi-dimensional details and enrich the semantic information during the whole feature fusion process. The experimental results show that compared with other object detection models, our model achieves 95.4% precision, 93.9% recall, 96.9% mAP50, and 74.8% mAP50:95 on the Br35H dataset,and 47.1% precision, 86.1% recall, 54.0% mAP50, and 38.7% mAP50:95 on the Ultralytics brain tumor dataset. These numbers not only highlight the effectiveness of the proposed model in brain tumor detection, but also provide new ideas for the subsequent application of object detection in medical imaging and clinical disease diagnosis. |
