Tuberculosis (TB) resides a significant health challenge, particularly in regions with limited access to medical resources. It spreads through the air and mainly affects the lungs, and without early detection, it can be deadly. Unfortunately, many areas don’t have enough trained healthcare workers or proper screening tools, which delays diagnosis.To help with this issue, we’re currently working on building an AI-based system that can automatically detect TB using chest X-ray images. Our approach involves a multi model deep learning system that combines two architectures: DeiT (Data-efficient Vision Transformer) and ResNet-16. DeiT helps the model understand the overall structure of the image through attention mechanisms, while ResNet-16 focuses on capturing detailed features in specific regions, all while keeping the system lightweight and efficient. We're using the TBX11K dataset , featuring three types of chest X-rays: healthy, non-TB but sick, and TBpositive. To make the system more transparent, we’re also adding heatmap visualizations using class activation mapping, so we can actually see which parts of the image the model is focusing on while making predictions. Our target is to design a model that’s not only accurate—hopefully hitting above 99% in key metrics—but also fast, with a prediction time of under 5 milliseconds. Once complete, we aim to make it suitable for real-time use in hospitals and clinics, especially in areas where medical support is limited.
K. Hemalatha (Sat,) studied this question.