Towards Precision Medicine: Deep Learning-Based Pulmonary Tuberculosis Detection in Radiographic Imaging

The approach taken here uses a deep learning-based one for detecting pulmonary tuberculosis from chest radiographic images for the improvement of precision medicine in the diagnosis of TB. Our approach will use a CNN model and transfer learning in order to automate and enhance the accuracy of the process of detecting TB so that, consequently, clinicians can attain earlier and more reliable diagnosis. The proposed model was trained and validated on a large dataset of X-ray images with a 91.2% accuracy, 92.5% sensitivity, and 90.3% specificity. A comparative analysis has demonstrated that the superiority of the proposed model towards traditional methods involves a significant improvement in diagnostic capabilities for diverse patient populations. The research thereby underlines promising advancements of AI-driven tools for the amelioration of healthcare outcomes, especially in resource-limited settings where TB is commonly found.