This paper is in the following e-collection/theme issue:

Digital Health Reviews (1724) Artificial Intelligence (2129) Pulmonology, COPD (195) Infectious Diseases (non-STD/STI) (1726) Machine Learning (2076) Computer-Aided Diagnosis (251)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43154, first published .
Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

Authors of this article:

Seng Hansun1, 2 Author Orcid Image ;   Ahmadreza Argha3, 4, 5 Author Orcid Image ;   Siaw-Teng Liaw6 Author Orcid Image ;   Branko G Celler7 Author Orcid Image ;   Guy B Marks1, 2 Author Orcid Image
Article Authors Cited by (50) Tweetations (6) Metrics

Journals

  1. Liang X, Tang K, Ke X, Jiang J, Li S, Xue C, Deng J, Liu X, Yan C, Gao M, Zhou J, Zhao L. Development of an MRI‐Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor. Journal of Magnetic Resonance Imaging 2024;60(2):523 View
  2. Özkurt C. Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging. Journal of Mathematical Sciences and Modelling 2024;7(1):33 View
  3. Chen B, Sheng W, Wu Z, Ma B, Cao N, Li X, Yang J, Yuan X, Yan L, Zhu G, Zhou Y, Huang Z, Zhu M, Ding X, Du H, Wan Y, Gao X, Cheng X, Xu P, Zhang T, Tao K, Shuai X, Cheng P, Gao Y, Zhang J. Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study. International Journal of Surgery 2024;110(6):3527 View
  4. Too E, Mwathi D, Gitonga L, Mwaka P, Kinyori S. An X-ray image-based pruned dense convolution neural network for tuberculosis detection. Computer Methods and Programs in Biomedicine Update 2024;6:100169 View
  5. Xu Y, Kang W, Sun W, Tong H, Ke W, Lyu E. Towards multi-view sputum smear quality classification. Biomedical Signal Processing and Control 2025;102:107217 View
  6. Dhivyaa C, Nithya K, Sathiya Kumar C, Sudhakar R. Explainable Model of Fusion Network With Enhanced Optimization Approach for Tuberculosis Diagnosis. IEEE Access 2024;12:176920 View
  7. Argha A, Alinejad-Rokny H, Baumgartner M, Schreier G, Celler B, Redmond S, Butcher K, Ooi S, Lovell N. A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms. IEEE Transactions on Biomedical Engineering 2025;72(2):833 View
  8. Yang Y, Xing W, Liu Y, Li Y, Ta D, Song Y, Hou D. Medical imaging-based artificial intelligence in pneumonia: A narrative review. Neurocomputing 2025;630:129731 View
  9. Kumar S, Rayal S, Bommaraju R, Varasala N, Papineni S, Deo S. Understanding Providers’ Attitude Toward AI in India’s Informal Health Care Sector: Survey Study. JMIR Formative Research 2025;9:e54156 View
  10. Eisentraut L, Mai C, Hosch J, Benecke A, Penava P, Buettner R. Deep Learning-Based Detection of Tuberculosis Using a Gaussian Chest X-Ray Image Filter as a Software Lens. IEEE Access 2025;13:36065 View
  11. Kaewwilai L, Yoshioka H, Choppin A, Prueksaritanond T, Ayuthaya T, Brukesawan C, Matupumanon S, Kawabe S, Shimahara Y, Phosri A, Kaewboonchoo O. Development and evaluation of an artificial intelligence (AI) -assisted chest x-ray diagnostic system for detecting, diagnosing, and monitoring tuberculosis. Global Transitions 2025;7:87 View
  12. Zhang S, Bei C, Li M, Zeng J, Yang L, Ren T, Deng G, Hong R, Cai J, Li D, Wang C, Xu P, Takiff H, Lu S, Zhang P, Gao Q. Identification and evaluation of blood transcriptional biomarker for tuberculosis screening. International Journal of Infectious Diseases 2025;153:107838 View
  13. Hansun S, Argha A, Bakhshayeshi I, Wicaksana A, Alinejad-Rokny H, Fox G, Liaw S, Celler B, Marks G. Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review. Journal of Medical Internet Research 2025;27:e69068 View
  14. Cremaschi M, Ditolve D, Curcio C, Panzeri A, Spoto A, Maurino A. Decoding the mind: A RAG-LLM on ICD-11 for decision support in psychology. Expert Systems with Applications 2025;279:127191 View
  15. Hansun S, Argha A, Alinejad-Rokny H, Alizadehsani R, Gorriz J, Liaw S, Celler B, Marks G. A New Ensemble Transfer Learning Approach With Rejection Mechanism for Tuberculosis Disease Detection. IEEE Transactions on Radiation and Plasma Medical Sciences 2025;9(4):433 View
  16. Nansamba B, Nakatumba-Nabende J, Katumba A, Kateete D. A Systematic Review on Application of Multimodal Learning and Explainable AI in Tuberculosis Detection. IEEE Access 2025;13:62198 View
  17. Wang X, Zhang S, Wei D, Zhang J, Cao ·. TMscNet: a model with multiple information interaction for COVID-19 X-ray classification. International Journal of Machine Learning and Cybernetics 2025;16(10):7449 View
  18. Dasarwar P, Yadav U, Morris K, Chavhan N, Bondre S, Kalamkar S. Revolutionizing Tuberculosis Prediction: A Cutting-Edge Approach. Engineering, Technology & Applied Science Research 2025;15(3):22929 View
  19. Zenner D, Haghparast-Bidgoli H, Chaudhry T, Abubakar I, Cobelens F. How to diagnose TB in migrants? A systematic review of reviews and decision tree analytical modelling exercise to evaluate properties for single and combined tuberculosis screening tests. European Respiratory Journal 2025;66(1):2402000 View
  20. Wang F, Wang H. Silent pulmonary tuberculosis in patients with poorly controlled diabetes mellitus: Pathogenesis, clinical implications, and diagnostic challenges. IDCases 2025:e02357 View
  21. Lemin M, Bustinduy A, Roberts C. Visual diagnostics for female genital schistosomiasis and the opportunity for improvement using computer vision. Parasitology 2025:1 View
  22. Jaiwant Joshi H, Barhate M, Prabhakar More K, Abin D, Murumkar R, Kumar Singh V. Machine learning-based early detection of tuberculosis in asymptomatic high-risk populations. Indian Journal of Tuberculosis 2025 View
  23. Paulenka D, Kosareva A, Snezhko E, Kovalev V. Algorithm for lung pathology detection in X-ray images using binary classification with emphasis on preprocessing. Informatics 2025;22(3):7 View
  24. Kai C, Kasai S, Teramoto R, Yoshida A, Tamori H, Kondo S, Hai P, Cong N, Tuan D, Loc T, Kodama N. Classifying abnormalities in chest radiographs from Vietnam using deep learning for early detection of cardiopulmonary diseases. Frontiers in Radiology 2025;5 View

Books/Policy Documents

  1. Ahamed M, Nahiduzzaman M, Islam M, Rahman T, Islam K, Altyeb A, Chowdhury M. Surveillance, Prevention, and Control of Infectious Diseases. View
  2. Yousif D, Mesilhy R, Aly R, Hegazi S, Yousif Z, Cyprian F, Abdallah A. Surveillance, Prevention, and Control of Infectious Diseases. View
  3. Juárez-Gonzalez B, Villalba-Meneses F, Cruz-Varela J, Tirado-Espín A, Vizcaino-Imacaña P, Cadena-Morejon C, Guevara C, Almeida-Galárraga D. Information and Communication Technologies. View
  4. Koul A, Bawa R, Kumar Y. Artificial Intelligence and Speech Technology. View
  5. Srinivasan S, Vallipriya R, Singh A. Public Private Partnership Dynamics for Economic Development. View
  6. Ayyachamy S, Manimaran M, Yadalam P, Anegundi R. Navigating Innovations and Challenges in Travel Medicine and Digital Health. View
  7. Anuraag B, Bandopadhyay S, Banerjee S. Computational Intelligence in Pattern Recognition. View
  8. Aslam A, Mustafa A, Boota N, Aziz S. Artificial Intelligence in Business. View
  9. Farnia P, Velayati A, Ghanavi J, Farnia P. Proteins in Mycobacterium Tuberculosis. View

Conference Proceedings

  1. Rama A, Rajakumar M, Mythili N, Arunmozhi S, Mohammed M, Rajinikanth V. 2023 International Conference on System, Computation, Automation and Networking (ICSCAN). Detection of TB from Chest X-ray: A Study with EfficientNet View
  2. Bhosale R, Yadav D. 2023 Second International Conference on Informatics (ICI). Analysis of EfficientNet Family Models by Retraining for Tuberculosis Detection from Chest X-Ray Images View
  3. Rabby M, Islam O, Assaduzzaman M, Dutta M. 2023 26th International Conference on Computer and Information Technology (ICCIT). Tuberculosis Disease Detection from Chest X-rays Using Deep Learning Techniques View
  4. Gonela L, B B, D B, S Y. 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS). Performance Analysis on Various Tuberculosis Detection Techniques and Remedial Suggestions Using Deep Learning View
  5. Dewantoro T, Edi Nugroho L, Permanasari A. 2024 International Electronics Symposium (IES). Implementing Crowdsourcing in Smart Government: An IT Perspective Review View
  6. Varshith T, Koneri T, Reddy T, Singh R. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). An Ensemble Approach to Tuberculosis Prediction using Shenzhen and Montgomery Datasets View
  7. Polisetty E, Sayeed S, Duttala O, Senthamarai.N . 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). Detection and Classification of Tuberculosis Disease Using Hybrid Deep Learning Method View
  8. Hansun S, Argha A, Alinejad-Rokny H, Liaw S, Celler B, Marks G. 2024 5th International Conference on Biomedical Engineering (IBIOMED). Pulmonary Tuberculosis Detection Using an Ensemble of ConvNeXts View
  9. Sidume F, Muchuchuti S, Chengetenai G, Tamukate R, Ntwaetsile K, Ntekeng P. 2024 International Conference on Electrical and Computer Engineering Researches (ICECER). Deep Learning Techniques for Tuberculosis Detection on Chest X-Rays in Low-Resource Settings: A Survey of Opportunities and Challenges View
  10. Singh R, Malviya J, Sabita , Singh R, Garg D. 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI). Hybrid Approach of Deep Convolution Neural Network and Transfer Learning for Tuberculosis Prediction View
  11. Kamal Zaidi S, Kabir M, Mahalakshmi G, Rajinikanth V. 2025 International Conference on Frontier Technologies and Solutions (ICFTS). Tuberculosis Detection in X-Ray with Two-Fold Training and Fused Deep Features Based Classification View
  12. Agarwal A, Maitra A, Srivastava P, Vijayvargiya D, C L B. 2025 International Conference on Electronics, AI and Computing (EAIC). Explainable AI Based TB Prognosis Using X-ray Images View
  13. Mary M, Arul U, Ramamoorthy M, Manimaran A, Rajaram G. 2025 10th International Conference on Applying New Technology in Green Buildings (ATiGB). Tuberculosis Detection in X-Ray with Two-Fold Training and Fused Deep Features Based Classification View
  14. Sobhan S, Zami A, Ahmed M, Zihan T, Khan T, Saha A. 2025 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM). A Multi-Stage Deep Learning Approach to Tuberculosis Detection with Explainable Insights View
  15. Xu Y, Lyu E. 2025 6th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). MSQC-UNet: A UNet-based Multi-view Sputum Quality Classification Network View
  16. Mary Antony A, Al-Assaf K, Vaiyapuri T, Vijayakumar K, D K, Jain A, Ahammed M. 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN). Detection of Tuberculosis from Chest X-ray using Deep Transfer Learning View
  17. Vijayakumar K, Elrashidi A, Waseem Zaidi S, Srinija K, Priya G, Varun G. 2025 2nd International Conference on Computing and Data Science (ICCDS). Automatic Detection of TB in X-Ray with Deep Learning Model View