Pulmonary Tuberculosis Detection from Chest X-ray Using Deep Learning

Pulmonary Tuberculosis Detection from Chest X-ray Using Deep Learning

Prof. Rushikesh Bhalerao1, Krushna Darekar2, Nikhil Kokate3, Nikita Nagare4, Komal Sangale5

1 Assistant Professor, Department of Information Technology, Sir Visvesvaraya Institute of

Technology, Nashik, Maharashtra, India.

2,3,4,5 Department of Information Technology, Sir Visvesvaraya Institute of Technology, Nashik,

Maharashtra, India.

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Abstract - Tuberculosis remains a formidable infectious disease, ranking among the top ten causes of global mortality. Timely detection is critical for effective treatment, yet current diagnostic methods face significant challenges. In this study, we propose a novel approach for automating tuberculosis detection from chest X-ray images. Our method integrates graph cut segmentation with convolutional neural network (CNN) classification, achieving an impressive accuracy of 94%, sensitivity of 96%, and specificity of 84%. This innovative approach holds promise for improving tuberculosis diagnosis, facilitating early intervention, and ultimately contributing to global tuberculosis control efforts.

Key Words:  Chest X-ray (CXR), Convolutional Neural Network (CNN), deep learning, graph cut, tuberculosis detection, automatic diagnosis.