Automatic Tuberculosis Prediction with Chest X-Ray using Deep Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802400Keywords:
Tuberculosis Detection, Chest X-ray Imaging, Deep Learning, Gated Attention U-Net, Medical Image Classification, Computer-Aided Diagnosis (CAD), Image SegmentationAbstract
Tuberculosis (TB) is one of the most common infectious diseases worldwide and remains a serious public health issue, particularly in developing countries. Early detection and timely treatment are crucial to control the spread of this disease. Typically, TB diagnosis relies on laboratory tests like sputum analysis and manual review of chest X-ray images by radiologists. However, these methods can take time and may be limited by the number of trained medical professionals available.
In recent years, deep learning techniques have made great strides in medical image analysis. This project suggests an automatic TB prediction system that uses chest X-ray images and deep learning models. The system employs g to analyze X-ray images and classify gated attention based 3D unet them as either TB-positive or normal. This approach aims to support healthcare workers by providing a fast, accurate, and automated diagnostic tool. By using deep learning and medical imaging, the system could enhance early detection and lessen the diagnostic burden in healthcare facilities. The experimental results show that the proposed model achieves 99.96% accuracy, demonstrating better performance compared with previous methods and providing a reliable tool to assist in tuberculosis detection.
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