A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.

ABSTRACT:

Introduction

Tuberculosis (TB) stands as a paramount global health concern, contributing significantly to worldwide mortality rates. Effective containment of TB requires deployment of cost-efficient screening method with limited resources. To enhance the precision of resource allocation in the global fight against TB, this research proposed chest X-ray radiography (CXR) based machine learning screening algorithms with optimization, benchmarking and tuning for the best TB subclassification tasks for clinical application.

Methods

This investigation delves into the development and evaluation of a robust ensemble deep learning framework, comprising 43 distinct models, tailored for the identification of active TB cases and the categorization of their clinical subtypes. The propos

SUBMITTER: Sun X 

PROVIDER: S-EPMC11301748 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature