OS-DETR: End-to-end brain tumor detection framework based on orthogonal channel shuffle networks

Abstract

OrthoNets use the Gram-Schmidt process to achieve orthogonality among filters but do not impose constraints on the internal orthogonality of individual filters. To reduce the risk of overfitting, especially in scenarios with limited data such as medical image, this study explores an enhanced network that ensures the internal orthogonality within individual filters, named the Orthogonal Channel Shuffle Network ( OSNet). This network is integrated into the Detection Transformer (DETR) framework for brain tumor detection, resulting in the OS-DETR. To further optimize model performance, this study also incorporates deformable attention mechanisms and an Intersection over Union strategy that emphasizes the internal region influence of bounding boxes and the corner distance disparity. Experimental results on the Br35H brain tumor dataset demonstrate the significant advantages of OS-DETR over mainstream object detection frameworks. Specifically, OS-DETR achieves a Precision of 95.0%, Recall of 94.2%, mAP@50 of 95.7%, and mAP@50:95 of 74.2%. The code implementation and experimental results are available at https://github.com/dkx2077/OS-DETR.git.