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Tackling Tuberculosis: A Comparative Dive into Machine Learning for Tuberculosis Detection

arXiv — cs.CV•Wednesday, December 3, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • A recent study has investigated the use of machine learning models, specifically ResNet-50 and SqueezeNet, for diagnosing tuberculosis (TB) through chest X-ray images. The research utilized a dataset of 4,200 X-rays from Kaggle, highlighting the limitations of traditional diagnostic methods in resource-limited settings. Results indicated that SqueezeNet achieved a notable performance with a loss of 32% and accuracy metrics that underscore the potential of deep learning in TB detection.
  • This development is significant as it addresses the critical need for efficient TB diagnosis, particularly in areas with limited healthcare resources. By leveraging advanced machine learning techniques, the study aims to enhance diagnostic accuracy and speed, potentially leading to earlier treatment and better patient outcomes in combating this long-standing infectious disease.
  • The exploration of machine learning in medical diagnostics reflects a broader trend towards integrating artificial intelligence in healthcare. Similar advancements in other areas, such as brain tumor classification and monkeypox detection, underscore the growing reliance on deep learning technologies to improve diagnostic processes. This shift not only highlights the versatility of AI applications but also raises important discussions about the ethical implications and accessibility of such technologies in global health.
— via World Pulse Now AI Editorial System
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