Developing an Efficient Diagnostic Model for Tuberculosis Detection through Automated X-ray Image Analysis

Abstract

Tuberculosis (TB) remains a significant global health challenge, particularly in resource constrained regions with limited access to diagnostic facilities. Chest X-ray imaging, a widely available and cost-effective tool, plays a crucial role in the early detection and diagnosis of TB. However, manual interpretation of X-rays is often subjective, time-consuming, and prone to error. This study focuses on developing an efficient diagnostic model for tuberculosis detection through automated X-ray image analysis using advanced deep learning techniques. The proposed model leverages convolutional neural networks (CNNs) to extract meaningful features from chest radiographs and classify images as TB-positive or TB-negative with high accuracy. A robust dataset of annotated X-ray images is used to train and validate the model, ensuring its reliability across diverse patient demographics and imaging conditions. Key preprocessing steps, including image enhancement and augmentation, are employed to improve model performance and generalizability. Results demonstrate that the developed model achieves superior sensitivity and specificity compared to traditional diagnostic methods, highlighting its potential to aid clinicians in TB screening and diagnosis. The integration of this automated system into healthcare workflows can significantly reduce diagnostic time, enhance accuracy, and expand access to TB detection, particularly in underserved areas. This work underscores the transformative potential of artificial intelligence in combating global health challenges and paves the way for further innovations in medical imaging and diagnostics..