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A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images

  • S.I. : Emerging trends in AI & ML
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Abstract

Malaria is an infectious disease caused by Plasmodium parasites and is potentially human life-threatening. Children under 5 years old are the most vulnerable group with approximately one death every two minutes, accounting for more than 65% of all malaria deaths. The World Health Organization (WHO) encourages the research of appropriate methods to treat malaria through rapid and economical diagnostic. In this paper, we present a deep learning-based framework for diagnosing human malaria infection from microscopic images of thin blood smears. The framework is based on a direct segmentation and classification approach which relies on the analysis of the parasite itself. The framework permits to segment the Plasmodium parasite in the images and to predict its species among four dominant classes: P. Falciparum, P. Malaria, P. Ovale, and P. Vivax. A high potential of generalization with a competitive performance of our framework on inter-class data is demonstrated through an experimental study considering several datasets. Our source code is publicly available on https://github.com/Benhabiles-JUNIA/MalariaNet.

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Funding

This project has received funding from the Interreg 2 Seas programme 2014-2020 co-funded by the European Regional Development Fund under subsidy contract No. 2S05–043 H4DC.

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Authors and Affiliations

  1. UMR 8520 - IEMN - Institut d’Electronique de Microélectronique et de Nanotechnologie, Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, Junia, F-59000, Lille, France

    Ziheng Yang, Halim Benhabiles, Feryal Windal & Ruiwen He

  2. IEMN - BioMEMS group, Lille, France

    Ziheng Yang, Halim Benhabiles, Feryal Windal & Ruiwen He

  3. Department of Computer Science, IRIMAS, Université de Haute-Alsace, F-68100, Mulhouse, France

    Karim Hammoudi

  4. Université de Strasbourg, Strasbourg, France

    Karim Hammoudi

  5. LIMMS/CNRS-IIS, IRL 2820, The University of Tokyo, Lille, France

    Dominique Collard

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  1. Ziheng Yang
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Correspondence to Ziheng Yang.

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Yang, Z., Benhabiles, H., Hammoudi, K. et al. A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images. Neural Comput & Applic 34, 14223–14238 (2022). https://doi.org/10.1007/s00521-021-06604-4

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