Tuberculosis Detection

Tuberculosis (TB) detection research focuses on developing accurate and accessible diagnostic tools, primarily leveraging artificial intelligence (AI) to analyze chest X-rays and cough audio. Current efforts concentrate on improving the robustness and reliability of deep learning models, including convolutional neural networks (CNNs) and Vision Transformers (ViTs), often addressing challenges like data imbalance and limited labeled data through techniques such as self-supervised learning and few-shot learning. These advancements aim to improve early TB diagnosis, particularly in resource-limited settings where access to expert radiologists is scarce, ultimately contributing to better disease management and reduced mortality.