Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.

ABSTRACT:

Introduction

Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications.

Methods

The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gyneco

SUBMITTER: Taddese AA 

PROVIDER: S-EPMC10809847 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature