Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11): 196-205.doi: 10.11896/jsjkx.240900088

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Medical Image Target Detection Method Based on Multi-branch Attention and Deep Down- sampling

GU Chengjie1, MENG Yi2, ZHU Dongjun1, ZHANG Junjun1   

  1. 1 School of Public Safety and Emergency Management,Anhui University of Science and Technology,Hefei 231100,China
    2 School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan,Anhui 232000,China
  • Received:2024-09-13 Revised:2024-12-19 Online:2025-11-15 Published:2025-11-06
  • About author:GU Chengjie,born in 1985,Ph.D,professor.His main research interests include trusted network architecture and security in cyberspace.
    ZHU Dongjun,born in 1991,Ph.D,lecturer.His main research interests include deep learning and machine vision.
  • Supported by:
    National Natural Science Foundation of Special Fund for Research on National Major Research Instrument(52227901),National Key Research and Development Plan of the Ministry of Science and Technology (Yangtze River Delta Science and Technology Innovation Community Joint Research Special Fund)(2023CSJGG1103),Natural Science Research Project of Colleges and Universities in Anhui Province (2023AH051197) and Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology(2023yjrc33).

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Abstract: With the development of artificial intelligence technology,medical image detection based on deep learning has a wide application prospect in clinical practice.However,for some medical image target detection such as tumor and plaque,there are some problems,such as small area to be labeled,few features to be extracted and difficult to extract.To solve these problems,this paper proposes a medical image target detection method(MD-Det) based on multi-branch attention and deep subsampling.The feature extraction module(C2f-DWR) is introduced to extract multi-scale features and enhance the feature representation of the target.This paper designes a deep down-sampling module(D-down) to capture the context information in the image more effectively and enhance the feature extraction capability.The core idea is to combine average pooling and maximum pooling operations to make full use of their respective advantages to improve the feature extraction effect by fusing multiple sampling methods.The accuracy of target detection is improved while maintaining the computational efficiency.Then,a multi-branch attention(MA) mechanism is proposed,which extracts and weights features of different dimensions,with each branch extracting features of different dimensions of the input tensor,including spatial and channel features.By generating attention weights,important features are emphasized and weighted together.The feature extraction capability of the network is enhanced,and the detection perfor-mance of the model is improved.Finally,a new joint optimization strategy is proposed,which weights Wise-IoU loss and NWD loss to form a joint regression loss function to further improve the accuracy of target recognition.Experiments show that the proposed method can effectively improve the detection accuracy of the model in medical image targets,and the mAP0.5of the medical data sets Tumor and Liver are increased by 2.5 percentage points and 1.1 percentage points,respectively.

Key words: Deformable convolution, Target detection, YOLO, Attention mechanism

CLC Number: 

  • TP391
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