Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays

TL;DR

This paper presents a deep learning pipeline that accurately screens for pulmonary tuberculosis using chest X-ray images, significantly improving diagnostic performance and aiding low-resource settings.

Contribution

The authors develop a novel deep learning system combining multiple architectures, image processing, and hyperparameter tuning for automated TB detection from X-rays.

Findings

Achieved 97.1% classification accuracy

Improved sensitivity and specificity over existing methods

Provided an automated, high-accuracy TB screening tool

Abstract

Tuberculosis (TB) is a contagious bacterial airborne disease, and is one of the top 10 causes of death worldwide. According to the World Health Organization (WHO), around 1.8 billion people are infected with TB and 1.6 million deaths were reported in 2018. More importantly,95% of cases and deaths were from developing countries. Yet, TB is a completely curable disease through early diagnosis. To achieve this goal one of the key requirements is efficient utilization of existing diagnostic technologies, among which chest X-ray is the first line of diagnostic tool used for screening for active TB. The presented deep learning pipeline consists of three different state of the art deep learning architectures, to generate, segment and classify lung X-rays. Apart from this image preprocessing, image augmentation, genetic algorithm based hyper parameter tuning and model ensembling were used to to improve the diagnostic process. We were able to achieve classification accuracy of 97.1% (Youden's index-0.941,sensitivity of 97.9% and specificity of 96.2%) which is a considerable improvement compared to the existing work in the literature. In our work, we present an highly accurate, automated TB screening system using chest X-rays, which would be helpful especially for low income countries with low access to qualified medical professionals.