考虑多域特征的改进Transformer模型用于结缔组织相关间质性肺病CT图像识别

doi:10.3969/j.issn.0258-8021.2026.03.003

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Abstract Connectivetissue disease-related interstitial lung disease (CTD-ILD) is a type of chronic respiratory disease with an increasing number of patients. Early detection and diagnosis of this disease can effectively improve patients' treatment outcomes and survival rates. Pulmonary imaging is a primary method for the early diagnosis of CTD-ILD. To extract valuable information quickly from many medical images and determine the severity of the disease, an improved Transformer model considering multi-domain features was proposed in this work. This model was based on multi-domain feature collaborative learning, integrating the ResNet and Vision Transformer (ViT). It used the self-attention mechanism in the complex lesion area and depthwise separable convolution in the simple non-lesion area for feature extraction. The experimental dataset comprised 2,752 CTD-ILD lung CT images labeled by professional physicians. The model's validity was systematically evaluated through comparative experiments, ablation studies, and robustness assessments under noisy conditions. On the self-built dataset, the model achieved an accuracy of 96.71 %, precision of 96.74 %, recall of 96.71 %, and F1 score of 96.69 %, representing a 5.83 % accuracy improvement over ResNet50 and outperforming 9 classic models. The average single training epoch took 17 minutes, 5 minutes shorter than the unimproved model, with only 28.7 seconds required to train a single image. Through multi-domain feature fusion and optimization of the ViT architecture, the proposed method has demonstrated excellent performance in medical image recognition for CTD-ILD, achieving a dual improvement in the image recognition performance and training efficiency. This method is expected to assist clinicians in diagnosis and improve diagnostic efficiency.

Key words： connective tissue disease-related interstitial lung disease ResNet improved Transformer dynamic self-attention mechanism

Received: 29 April 2025

PACS:

R318

Corresponding Authors: ^*E-mail:erheyan@163.com

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	Articles by authors
	Gao Jing
	Li Lei
	Zhu Lingyan
	Qian Tianhe
	Wang Yongfu

Cite this article:

Gao Jing,Li Lei,Zhu Lingyan, et al. Using Improved Transformer Model Considering Multi-domain Features for CT Image Recognitionof Connective Tissue-Associated Interstitial Lung Disease[J]. Chinese Journal of Biomedical Engineering, 2026, 45(3): 276-287.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2026.03.003 OR http://cjbme.csbme.org/EN/Y2026/V45/I3/276

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