Vol. 3 No. 1 (2025): Issue - 1
Articles

Automated Tuberculosis Detection from Chest X-Rays Using a ResNet50 Architecture

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  • Sreelakshmi Induri,
  • M Reddi Durgasree,
  • Beeda Sukumar,
  • G Ramasubba Reddy,
  • Y Subba Reddy,
  • J Jagadeswara Reddy
Sreelakshmi Induri
Department of Computer Science, St. Francis College for Women, Begumpet, Hyderabad, India
M Reddi Durgasree
Department of CSE (AI&ML), Guru Nanak Institutions Technical Campus, Hyderabad, India
Beeda Sukumar
Department of ECE, Narayana Engineering College, Nellore, India
G Ramasubba Reddy
Department of Computer Science Engineering, Sai Rajeswari Institute of Technology, Proddatur, Andhra Pradesh, India.
Y Subba Reddy
Department of Computer Science Engineering, Sai Rajeswari Institute of Technology, Proddatur, Andhra Pradesh, India.
J Jagadeswara Reddy
Department of Computer Science Engineering, Sai Rajeswari Institute of Technology, Proddatur, Andhra Pradesh, India.
DOI: https://doi.org/10.5281/zenodo.14903623

Published 2025-02-21

Keywords

  • TB Diagnosis,
  • Deep Learning,
  • Chest X-Rays,
  • Interpretability,
  • ResNet50,
  • LIME
    • ...More
    Less

How to Cite

Sreelakshmi Induri, M Reddi Durgasree, Beeda Sukumar, G Ramasubba Reddy, Y Subba Reddy, & J Jagadeswara Reddy. (2025). Automated Tuberculosis Detection from Chest X-Rays Using a ResNet50 Architecture. Milestone Transactions on Medical Technometrics, 3(1), 133–144. https://doi.org/10.5281/zenodo.14903623

Abstract

TB (tuberculosis) is still a major worldwide health concern, and in order to control its spread and enhance patient outcomes, precise and prompt diagnostic methods are required. This study introduces a deep-learning methodology employing a ResNet50 architecture for the reliable and precise classification of TB in CXR images. The model utilizes a robust CNN (Convolutional Neural Network) to extract essential features, enhanced by image pre-processing techniques to optimize image quality and overall performance. The ResNet50 classifier shows great performance metrics, achieving an accuracy of 99.82%, a 99.82% accuracy rate and an AUC of 99.87%. These results highlight the potential of ResNet50 as a valuable TB detection tool for healthcare professionals, particularly in resource-constrained environments, enabling faster diagnosis and treatment.
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