Image Analysis and Machine Learning for Detecting Malaria

Overview

Journal Transl Res

Publisher Elsevier

Specialty Pathology

Date 2018 Jan 24

PMID 29360430

Citations 90

Authors

Mahdieh Poostchi

Kamolrat Silamut

Richard J Maude

Stefan Jaeger

George Thoma

Affiliations

Soon will be listed here.

Abstract

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.

Citing Articles

A 5G network based conceptual framework for real-time malaria parasite detection from thick and thin blood smear slides using modified YOLOv5 model.

Lipsa S, Dash R, Cengiz K, Ivkovic N, Akhunzada A Digit Health. 2025; 11:20552076251321540.

PMID: 40078448 PMC: 11898240. DOI: 10.1177/20552076251321540.

Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears.

Sora-Cardenas J, Fong-Amaris W, Salazar-Centeno C, Castaneda A, Martinez-Bernal O, Suarez D Sensors (Basel). 2025; 25(2).

PMID: 39860759 PMC: 11769203. DOI: 10.3390/s25020390.

Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.

Contreras-Ramirez M, Sora-Cardenas J, Colorado-Salamanca C, Ovalle-Bracho C, Suarez D Sensors (Basel). 2025; 24(24.

PMID: 39771915 PMC: 11679136. DOI: 10.3390/s24248180.

An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images.

Boit S, Patil R Diagnostics (Basel). 2024; 14(23).

PMID: 39682645 PMC: 11639908. DOI: 10.3390/diagnostics14232738.

Digital Holographic Microscopy in Veterinary Medicine-A Feasibility Study to Analyze Label-Free Leukocytes in Blood and Milk of Dairy Cows.

Farschtschi S, Lengl M, Rohrl S, Klenk C, Hayden O, Diepold K Animals (Basel). 2024; 14(21).

PMID: 39518879 PMC: 11544890. DOI: 10.3390/ani14213156.

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