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

Computer-aided diagnosis systems for automatic malaria parasite detection and classification: a systematic review / Grignaffini, F.; Simeoni, P.; Alisi, A.; Frezza, F.. - In: ELECTRONICS. - ISSN 2079-9292. - 13(2024). [10.3390/electronics13163174]

Computer-aided diagnosis systems for automatic malaria parasite detection and classification: a systematic review

F. Grignaffini;P. Simeoni;A. Alisi;F. Frezza
2024

Abstract

Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise from pathologists. Early diagnosis of this disease is necessary to achieve timely and effective treatment, which avoids tragic consequences, thus leading to the development of computer-aided diagnosis systems based on artificial intelligence (AI) for the detection and classification of blood cells infected with the malaria parasite in blood smear images. Such systems involve an articulated pipeline, culminating in the use of machine learning and deep learning approaches, the main branches of AI. Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in blood smear images. Based on the PRISMA 2020 criteria, a search was conducted using several electronic databases including PubMed, Scopus, and arXiv by applying inclusion/exclusion filters. From the 606 initial records identified, 135 eligible studies were selected and analyzed. Many promising results were achieved, and some mobile and web applications were developed to address resource and expertise limitations in developing countries.
2024
malaria; parasite detection; blood smear images; optical microscope; computer-aided diagnostics; artificial intelligence; machine learning; deep learning; web applications; mobile devices
01 Pubblicazione su rivista::01a Articolo in rivista
Computer-aided diagnosis systems for automatic malaria parasite detection and classification: a systematic review / Grignaffini, F.; Simeoni, P.; Alisi, A.; Frezza, F.. - In: ELECTRONICS. - ISSN 2079-9292. - 13(2024). [10.3390/electronics13163174]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1717460
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