Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review

Electronics, Journal Year: 2024, Volume and Issue: 13(16), P. 3174 - 3174

Published: Aug. 11, 2024

Malaria is a disease that affects millions of people worldwide with consistent mortality rate. The light microscope examination the gold standard for detecting infection by malaria parasites. Still, it limited long timescales and requires high level expertise from pathologists. Early diagnosis this necessary to achieve timely effective treatment, which avoids tragic consequences, thus leading development computer-aided systems based on artificial intelligence (AI) detection classification blood cells infected parasite in smear images. Such involve an articulated pipeline, culminating use machine learning deep approaches, main branches AI. Here, we present systematic literature review recent research automated algorithms identify classify parasites Based PRISMA 2020 criteria, search was conducted using several electronic databases including PubMed, Scopus, arXiv applying inclusion/exclusion filters. From 606 initial records identified, 135 eligible studies were selected analyzed. Many promising results achieved, some mobile web applications developed address resource limitations developing countries.

Language: Английский

Deep learning method for malaria parasite evaluation from microscopic blood smear

Artificial Intelligence in Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 103114 - 103114

Published: March 1, 2025

Language: Английский

Involution-based efficient autoencoder for denoising histopathological images with enhanced hybrid feature extraction

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 192, P. 110174 - 110174

Published: April 24, 2025

Language: Английский