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

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.