Recognition of parasitic helminth eggs via a deep learning-based platform.

ABSTRACT:

Introduction

Accurate and rapid diagnosis is crucial for the effective treatment of parasitosis. Traditional etiological methods, especially microscopic examination, are time-consuming, labor-intensive, and prone to false or missed detections. In response to these challenges, this study explores the use of artificial intelligence (AI) for the detection and classification of human parasite eggs through the YOLOv4 deep learning object detection algorithm.

Methods

Eggs from species such as Ascaris lumbricoides (A. lumbricoides), Trichuris trichiura (T. trichiura), Enterobius vermicularis (E. vermicularis), Ancylostoma duodenale (A. duodenale), Schistosoma japonicum (S. japonicum

SUBMITTER: He W 

PROVIDER: S-EPMC11614813 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature