Sélection de la langue

Gouvernement du Canada / Government of Canada

Recherche

Empowering tuberculosis screening with explainable self-supervised deep neural networks

DOITrouver le DOI : https://doi.org/10.1109/ICMLA61862.2024.00113
AuteurRechercher : ; Rechercher : ; Rechercher : 1Identifiant ORCID : https://orcid.org/0000-0002-4542-9105
Affiliation
  1. Conseil national de recherches Canada. Technologies numériques
FormatTexte, Article
Conférence2024 International Conference on Machine Learning and Applications, ICMLA, December 18 - 20, 2024, Miami, Florida, United States
Sujettuberculosis; self-supervised learning; explainable neural network; rapid screening; radiology
Résumé
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.
Date de publication
Maison d’éditionIEEE
Dans
  • 2024 International Conference on Machine Learning and Applications, ICMLA (18 décembre 2024) : 794–797.
Langueanglais
Publications évaluées par des pairsOui
Exporter la noticeExporter en format RIS
Signaler une correctionSignaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement901e2839-6348-4ece-ba55-01e1b52a9d1d
Enregistrement créé2025-03-12
Enregistrement modifié2025-03-18

Détails de la page

Par :

Date de modification :