Leveraging Advanced Deep Learning techniques For Chest X-ray Tuberculosis Detection
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Date
2025
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Volume Title
Publisher
NHCE
Abstract
Tuberculosis (TB) continues to be a major global health issue, especially in low-income and developing regions, where it claims around 1.5 million lives annually, despite being both preventable and treatable. Traditional TB diagnosis through chest radiographs, analyzed by skilled radiologists, is often time-consuming, prone to variability in interpretation, and vulnerable to misclassification, particularly with other diseases that have similar radiologic features. These challenges are even more pronounced in rural and underserved areas where access to experienced radiologists is limited. To tackle these issues, this project proposes the creation of an automated deep learning-based system for TB detection using chest X-rays. By harnessing the power of Convolutional Neural Networks (CNNs), the system aims to enhance both diagnostic accuracy and accessibility.
The proposed system intends to streamline the detection process by learning deep features directly from raw image data, eliminating the need for manual feature extraction, which is typically required in conventional machine learning models. Previous studies have shown the efficacy of CNNs in medical image analysis, with numerous models achieving impressive accuracy rates. This project builds on these advancements to develop a robust deep learning model specifically designed for TB detection, focusing on improving classification performance, consistency, and reliability.
Key advancements in TB detection have been driven by the evolution of sophisticated CNN architectures and the availability of extensive annotated datasets, which have enabled the development of highly accurate models. This project will leverage cutting-edge deep learning techniques and rigorously evaluate the model's performance in various clinical contexts, ensuring its robustness across different healthcare settings. The primary goal is to create a system that not only matches but exceeds the diagnostic capabilities of human radiologists, offering a vital tool for early, precise TB detection, particularly in resource-constrained environments.
In addition to improving diagnostic efficiency, this system aims to provide a scalable solution that can be widely implemented across diverse healthcare systems, especially in regions with limited medical infrastructure. The system’s integration into clinical workflows could transform TB screening, making it faster, more reliable, and more accessible. By enhancing early detection capabilities, this project seeks to improve patient outcomes, reduce transmission rates, and contribute significantly to global efforts aimed at eradicating tuberculosis.