Abstract
Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. The identification of the parasitized blood cells is a laborious and challenging task as it involves very complex and time consuming methods such as spotting the parasite in the blood and counting the number of the parasites. This examination can be arduous for large-scale diagnoses, resulting in poor quality. This paper presents a new Convolutional Neural Network (CNN) architecture named P-FideNet aimed at the detection of Malaria. The proposed CNN model can be used to solve image classification problems of blood cells infected or not by parasite X. This tool makes the process of analysis by the specialist faster and more accurate. Comparative tests were carried out with state-of-the-art works, and P-FideNet achieved 98.53% recall, 98.88% accuracy and 99% precision.
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Cruz, D. et al. (2020). P-FideNet: Plasmodium Falciparum Identification Neural Network. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_29
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DOI: https://doi.org/10.1007/978-3-030-64556-4_29
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