Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

ABSTRACT:

Background

Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence-based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB.

Objective

This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]).

Methods

In conducting and reporting the SL

SUBMITTER: Hansun S 

PROVIDER: S-EPMC10365622 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature