ArticleMedical & biological engineering & computing2024
Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism.
Article in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 8 citations in OpenAlex.
- A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.Nursing & health sciences · 2025Pooled it
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- Comorbidity of chronic obstructive pulmonary disease and pulmonary tuberculosis: a bibliometric analysis (2011-2025) with narrative review.Journal of thoracic disease · 2026Article
- Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network.Journal of orthopaedic surgery and research · 2025Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.