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#xInfectiousDiseases (37)

Published on in Vol 6 (2025)

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2024.08.02.24311396v1, first published .
Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures

Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures

Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures

Authors of this article:

Alex Mirugwe1 Author Orcid Image ;   Lillian Tamale2 Author Orcid Image ;   Juwa Nyirenda3 Author Orcid Image
Article Authors Cited by (12) Tweetations (2) Metrics

Journals

  1. Pitakaso R. Peer Review of “Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures”. JMIRx Med 2025;6:e77171 View
  2. Mirugwe A, Tamale L, Nyirenda J. Authors’ Response to Peer Reviews of “Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures”. JMIRx Med 2025;6:e77221 View
  3. Nanthasamroeng N. Peer Review of “Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures”. JMIRx Med 2025;6:e77174 View
  4. Liu J, Sun P, Yuan Y, Chen Z, Tian K, Gao Q, Li X, Xia L, Zhang J, Xu N. YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study. JMIR Medical Informatics 2025;13:e79064 View
  5. Pala M, Navdar M. SPX-GNN: An Explainable Graph Neural Network for Harnessing Long-Range Dependencies in Tuberculosis Classifications in Chest X-Ray Images. Diagnostics 2025;15(24):3236 View
  6. Singthongchai J, Wangkhamhan T. Adaptive Normalization Enhances the Generalization of Deep Learning Model in Chest X-Ray Classification. Journal of Imaging 2025;12(1):14 View
  7. Hasan R, Ahanotu P, Adedigba D, Palaniappan S. Deep Learning-Based Automatic Detection and Diagnosis of Tuberculosis from Chest X-ray Images: A Comprehensive Analysis. Journal of Informatics and Web Engineering 2026;1(5):69 View
  8. Alsulami A, Abu Al-Haija Q, Alakhtar R, Alsobhi H, Alsemmeari R, Alturki B, Tayeb A. Improving Tree-Based Lung Disease Classification from Chest X-Ray Images Using Deep Feature Representations. Bioengineering 2026;13(3):267 View
  9. Tamale L, Ssebuggwawo D, Mirembe D, Mirugwe A, Lubega J. Artificial intelligence-powered multiclass deep learning model for detection of aflatoxin-related defects in Ugandan groundnuts. Discover Artificial Intelligence 2026 View

Conference Proceedings

  1. Teja M, Singh D. 2025 12th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). TBNet: A Deep Learning Framework for Automated Tuberculosis Detection from Chest X-Ray Images View
  2. Kurniawan N, Joshua R, Edbert I, Aulia A. 2025 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). Optimizing Custom Convolutional Blocks for Pre-trained CNNs in Tuberculosis X-ray Analysis via Bayesian Optimization View
  3. Ibrahim A, Alharith R, Altahir Mohammed A, Leau Y, On C, Much Ibnu Subroto I. 2025 International Conference on Advances in Machine Intelligence, and Cybersecurity Technologies (AMICT). MobileNet-based Tuberculosis Segmentation in Chest X-Rays: An Accuracy-Efficiency Trade-off Analysis View