NOVEL DEVICE FOR ENHANCING TUBERCULOSIS DIAGNOSIS FOR FASTER, MORE ACCURATE SCREENING RESULTS

Abstract

Tuberculosis (TB) continues to be a leading global cause of illness and death, highlighting an urgent need for rapid, precise diagnostic solutions. Conventional methods, such as chest X-rays and sputum smear microscopy, often lack adequate sensitivity and specificity, resulting in treatment delays and increased transmission. This research introduces a novel device leveraging deep learning to enhance TB diagnosis, utilizing a convolutional neural network (CNN) model designed to automatically detect TB-related abnormalities in chest X-ray images. The device, powered by a large annotated dataset, incorporates transfer learning to fine-tune pre-trained models, maximizing diagnostic performance. Key evaluation metrics—accuracy, precision, recall, and F1-score—demonstrate the model’s efficacy compared to traditional diagnostics. Preliminary findings show that the AI-driven approach significantly enhances diagnostic accuracy, reducing false negatives and facilitating faster screenings. These results suggest that embedding AI technology in clinical workflows can enable earlier TB detection, optimize healthcare resources, and improve patient outcomes. Future directions include testing the device across diverse clinical environments and exploring integration with mobile health technologies to broaden access to rapid TB diagnostics.