A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest Radiograph

Abstract

The high mortality rate in Tuberculosis (TB) burden regions has increased significantly in the last decades. Despite the possibility of treatment for TB, high burden regions still suffer inadequate screening tools, which result in diagnostic delay and misdiagnosis. These challenges have led to the development of Computer-Aided Diagnostic (CAD) system to detect TB automatically. There are several ways of screening for TB, but Chest X-Ray (CXR) is more prominent and recommended due to its high sensitivity in detecting lung abnormalities. This paper presents the results of a systematic review based on PRISMA procedures that investigate state-of-the-art Deep Learning techniques for screening pulmonary abnormalities related to TB. The systematic review was conducted using an extensive selection of scientific databases as reference sources that grant access to distinctive articles in the field. Four scientific databases were searched to retrieve related articles. Inclusion and exclusion criteria were defined and applied to each article to determine those included in the study. Out of the 489 articles retrieved, 62 were included. Based on the findings in this review, we conclude that CAD systems are promising in tackling the challenges of the TB epidemic and made recommendations for improvement in future studies.

Keywords: chest radiograph; computer-aided diagnosis; deep learning; systematic review; tuberculosis.

Figure 1

The PRISMA structure for the study selection process.

Figure 2

Articles selection by database and year.

Figure 3

Dataset frequency of usage.

Figure 4

Hierarchical chart of computational techniques according to the frequency of usage.