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Empowering tuberculosis screening with explainable self-supervised deep neural networks

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/ICMLA61862.2024.00113
AuthorSearch for: ; Search for: ; Search for: 1ORCID identifier: https://orcid.org/0000-0002-4542-9105
Affiliation
  1. National Research Council of Canada. Digital Technologies
FormatText, Article
Conference2024 International Conference on Machine Learning and Applications, ICMLA, December 18 - 20, 2024, Miami, Florida, United States
Subjecttuberculosis; self-supervised learning; explainable neural network; rapid screening; radiology
Abstract
Tuberculosis remains a global health crisis, disproportionately affecting resource-limited populations and remote regions, with over 10 million new infections annually. Though curable, early detection is crucial. Chest X-rays are the primary screening tool, but their use requires skilled radiologists, often unavailable in underserved areas. This highlights the need for AI-powered systems to assist in rapid screening. However, training reliable AI models requires large-scale, high-quality data, which is costly and challenging to obtain. To address this, we introduce an explainable self-supervised learning network for tuberculosis screening, achieving 98.14% accuracy, with recall and precision rates of 95.72% and 99.44%, respectively.
Publication date
PublisherIEEE
In
  • 2024 International Conference on Machine Learning and Applications, ICMLA (18 December 2024): 794–797.
LanguageEnglish
Peer reviewedYes
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Record identifier901e2839-6348-4ece-ba55-01e1b52a9d1d
Record created2025-03-12
Record modified2025-03-18
Date modified: