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<p>Tuberculosis (TB) remains a significant global health challenge, with millions of new cases and deaths reported annually. Early and accurate detection is crucial for controlling its spread and reducing morbidity. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system, SAM-LCA, for TB classification using chest X-ray (CXR) images. Built upon the Segment Anything Model (SAM-Med2D), our approach leverages its pre-trained image encoder for effective global representation learning. Additionally, we introduce two lightweight attention mechanisms, Improved Linear Attention (ILA) and Chunk-Wise Attention Block (CWAB), which significantly reduce computational complexity without compromising accuracy. The SAM-LCA model was evaluated on two public datasets, and demonstrated state-of-the-art performance in both binary and multi-class classification tasks. Extensive experiments, including ablation studies, show that SAM-LCA achieves near-perfect classification results with improved computational efficiency, making it well-suited for resource-constrained environments such as healthcare settings.</p>

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SAM-LCA: a computationally efficient SAM-based model for tuberculosis detection in chest X-rays

  • 期刊论文

  • 发表日期：

    2025年4月26日

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  • Xiaoyan Jiang,
  • Si-Yuan Lu,
  • Yu-Dong Zhang

摘要

Tuberculosis (TB) remains a significant global health challenge, with millions of new cases and deaths reported annually. Early and accurate detection is crucial for controlling its spread and reducing morbidity. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system, SAM-LCA, for TB classification using chest X-ray (CXR) images. Built upon the Segment Anything Model (SAM-Med2D), our approach leverages its pre-trained image encoder for effective global representation learning. Additionally, we introduce two lightweight attention mechanisms, Improved Linear Attention (ILA) and Chunk-Wise Attention Block (CWAB), which significantly reduce computational complexity without compromising accuracy. The SAM-LCA model was evaluated on two public datasets, and demonstrated state-of-the-art performance in both binary and multi-class classification tasks. Extensive experiments, including ablation studies, show that SAM-LCA achieves near-perfect classification results with improved computational efficiency, making it well-suited for resource-constrained environments such as healthcare settings.