ArticleDiagnostics (Basel, Switzerland)2024
Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Classification and Segmentation of Medical Images Using Cross-Representation Attention Fusion and Fuzzy Image Enhancement.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation.Diagnostics (Basel, Switzerland) · 2026Review
- Decision-Aware Vision Mamba with Context-Guided Slot Mixing for Chest X-Ray Screening and Culture-Based Hierarchical Tuberculosis Classification.Sensors (Basel, Switzerland) · 2026Article
- Interpretable lung-constrained RegNetY-ViT framework for pulmonary tuberculosis classification in chest X-rays with radiological feature-guided neuro-symbolic reasoning.Frontiers in medicine · 2026Article
- An efficient deep learning-based morphology aware hierarchical mixture of features for tuberculosis screening using segmentation of chest X-ray images.Scientific reports · 2025Article
- IHRAS: Automated Medical Report Generation from Chest X-Rays via Classification, Segmentation, and LLMs.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
7 authors.
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Abstract
We present a deep learning (DL) network-based approach for detecting and semantically segmenting two specific types of tuberculosis (TB) lesions in chest X-ray (CXR) images. In the proposed method, we use a basic U-Net model and its enhanced versions to detect, classify, and segment TB lesions in CXR images. The model architectures used in this study are U-Net, Attention U-Net, U-Net++, Attention U-Net++, and pyramid spatial pooling (PSP) Attention U-Net++, which are optimized and compared based on the test results of each model to find the best parameters. Finally, we use four ensemble approaches which combine the top five models to further improve lesion classification and segmentation results. In the training stage, we use data augmentation and preprocessing methods to increase the number and strength of lesion features in CXR images, respectively. Our dataset consists of 110 training, 14 validation, and 98 test images. The experimental results show that the proposed ensemble model achieves a maximum mean intersection-over-union (
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