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Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism

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Abstract

This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model’s output, which can identify lesions in important parts of the lungs and make transparent decisions.

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Funding

National Natural Science Foundation of China [NO. U20A20224]; tuberculosis classification test based on lung CT images [070909922114]; Science and Technology Program of Tongzhou District of Beijing [grant numbers KJ2022CX089].

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Authors and Affiliations

  1. Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China

    Shidong Zhang, Cong He, Zhenzhen Wan, Ning Shi & Xiuling Liu

  2. Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China

    Bing Wang & Dailun Hou

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  1. Shidong Zhang
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  2. Cong He
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  3. Zhenzhen Wan
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Correspondence to Cong He, Bing Wang or Dailun Hou.

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Zhang, S., He, C., Wan, Z. et al. Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism. Med Biol Eng Comput 62, 1589–1600 (2024). https://doi.org/10.1007/s11517-024-03022-1

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