Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

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.

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Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

Cited by 4 publications

References 13 publications

Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures

Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures

Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future

Image analysis and machine learning for detecting malaria

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