Abstract: Tuberculosis (TB) has remained a significant global health concern for a very long time. It necessitates accurate and early detection and curing to improve patient health. This study comprehensively reviews various methods employed for detecting TB using chest X-rays. It explores traditional diagnostic approaches, including manual interpretation by radiologists, and advances in automated techniques such as machine learning (ML) and deep learning (DL) algorithms. The paper highlights the strengths and limitations of different methodologies, focusing on their accuracy, sensitivity, specificity, and computational efficiency. This study aims to offer insights into the revolution of TB detection methods and inform about developing more robust and scalable tools.
Keywords: Tuberculosis, Deep learning, Machine learning, Early detection.
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DOI:
10.17148/IJARCCE.2025.14670
