mlsanityData to 7 Oct 2026

Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization

arXiv 2007.14895 ↗First posted 29 Jul 2020eess.IV cs.CVcited by 50

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Abstract (arXiv)

Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 700 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet), which were used for transfer learning from their pre-trained initial weights and trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images, and segmented lung images. The accuracy, precision, sensitivity, F1-score, specificity in the detection of tuberculosis using X-ray images were 97.07 %, 97.34 %, 97.07 %, 97.14 % and 97.36 % respectively. However, segmented lungs for the classification outperformed than whole X-ray image-based classification and accuracy, precision, sensitivity, F1-score, specificity were 99.9 %, 99.91 %, 99.9 %, 99.9 %, and 99.52 % respectively. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions results in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jul 2020.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 7; 2024: 12; 2025: 15; 2026: 4.

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  1. 2023Meta-Transformer: A Unified Framework for Multimodal Learningcited by 136
  2. 2021COVID-19 Infection Localization and Severity Grading from Chest X-ray Imagescited by 46
  3. 2022Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimizationcited by 25
  4. 2022NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Researchcited by 18
  5. 2022Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classificationcited by 16
  6. 2024Redefining Contributions: Shapley-Driven Federated Learningcited by 16
  7. 2025OmniVec2 -- A Novel Transformer based Network for Large Scale Multimodal and Multitask Learningcited by 13
  8. 2024A foundation model for generalizable disease diagnosis in chest X-ray imagescited by 12
  9. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  10. 2021TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Imagescited by 8
  11. 2022An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiographcited by 7
  12. 2025RepViT-CXR: A Channel Replication Strategy for Vision Transformers in Chest X-ray Tuberculosis and Pneumonia Classificationcited by 7
38 more
  1. 2021COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural networkcited by 6
  2. 2025Federated Learning for Medical Image Classification: A Comprehensive Benchmarkcited by 6
  3. 2022RamanNet: A generalized neural network architecture for Raman Spectrum Analysiscited by 5
  4. 2023On enhancing the robustness of Vision Transformers: Defensive Diffusioncited by 5
  5. 2022BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Datacited by 4
  6. 2023Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensemblescited by 4
  7. 2024Explore the Limits of Omni-modal Pretraining at Scalecited by 4
  8. 2021A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Imagescited by 3
  9. 2024A Novel Approach to Chest X-ray Lung Segmentation Using U-net and Modified Convolutional Block Attention Modulecited by 3
  10. 2023Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classificationcited by 2
  11. 2024Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Imagescited by 2
  12. 2024HYDEN: Hyperbolic Density Representations for Medical Images and Reportscited by 2
  13. 2024Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifiercited by 2
  14. 2021Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rayscited by 1
  15. 2024Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networkscited by 1
  16. 2024Exploring Compositional Generalization of Multimodal LLMs for Medical Imagingcited by 1
  17. 2024Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquirycited by 1
  18. 2025Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Surveycited by 1
  19. 2025Incentive-Based Federated Learning: Architectural Elements and Future Directionscited by 1
  20. 2025Weakly Supervised Concept Learning with Class-Level Priors for Interpretable Medical Diagnosiscited by 1
  21. 2025CLARiTy: A Vision Transformer for Multi-Label Classification and Weakly-Supervised Localization of Chest X-ray Pathologiescited by 1
  22. 2026Intelligent Documentation in Medical Education: Can AI Replace Manual Case Logging?cited by 1
  23. 2023SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classificationcited by 0
  24. 2023Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detectioncited by 0
  25. 2023How much data do I need? A case study on medical datacited by 0
  26. 2024INSITE: labelling medical images using submodular functions and semi-supervised data programmingcited by 0
  27. 2024Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Imagescited by 0
  28. 2025Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysiscited by 0
  29. 2025Pulmonary Tuberculosis Edge Diagnosis System Based on MindSpore Framework: Low-cost and High-precision Implementation with Ascend 310 Chipcited by 0
  30. 2025Interpretable Deep Learning Framework for Improved Disease Classification in Medical Imagingcited by 0
  31. 2025Novel Pooling-based VGG-Lite for Pneumonia and Covid-19 Detection from Imbalanced Chest X-Ray Datasetscited by 0
  32. 2025MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classificationcited by 0
  33. 2025VGS-ATD: Robust Distributed Learning for Multi-Label Medical Image Classification Under Heterogeneous and Imbalanced Conditionscited by 0
  34. 2025FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspectivecited by 0
  35. 2025An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detectioncited by 0
  36. 2026HyPCA-Net: Advancing Multimodal Fusion in Medical Image Analysiscited by 0
  37. 2026SecondOpinion: Anatomy-Aware Gated Reasoning for Efficient Medical Image Analysiscited by 0
  38. 2026Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detectioncited by 0