Deep Learning-based Diagnosis and Impact Visualization of Tuberculosis using CXR Scanned Images

Tuberculosis (TB) is one of the foremost global issues, especially in resource-poor setups that lack diagnostic equipment. This study seeks to generate highly advanced models using deep learning algorithms in order to autonomously detect tuberculosis and precisely visualize the affected areas of the lungs on CXR scan images. The system proposes the employment of convolutional neural networks (CNNs) trained on large annotated datasets of chest X-ray scans to distinguish between healthy and tuberculosis-infected lung tissues. The model will achieve detection with visualization techniques such as Grad-CAM to distinctly highlight the damaged regions to assist professionals in assessing the severity of the illness and provide them with greater strength in their decision-making processes. The project aims to establish a very efficient and scalable AI-based diagnostic tool in clinical settings, especially in locations lacking trained radiologists, thus facilitating early diagnosis and improving patient prognosis. This study responds to the urgent needs of better diagnostics for tuberculosis while edging global health concern.