A Comparative Analysis of Deep Learning Models for Malaria Plasmodium Classification

Malaria, caused by the Plasmodium parasite and transmitted by Anopheles mosquitoes, poses a significant health threat in sub-Saharan Africa, especially in resource-limited settings. This study aims to address this by analyzing three advanced digital and biomedical technologies in malaria detection and species identification. We used a dataset of 502 Giemsa-stained thick blood smear images from Rwanda to evaluate the efficacy of these models in detecting and classifying four Plasmodium species: P. falciparum, P. malariae, P. ovale, and P. vivax. The results showed that YOLOv5 demonstrated superior overall performance, par-ticularly in multi -class detection scenarios, suggesting its potential for early and accurate diagnosis in real-world applications. Mask R-CNN showed the best performance in detecting P. falciparum, while Faster R-CNN exhibited consistent performance across all species. This research contributes to the development of computer-aided diagnostic tools tailored for regions with limited resources, aiming to enhance malaria control strategies in developing areas.

Recommended citation: C. P. Mukamakuza, A. D. Nishimwe Karasira, E. M. Akpo, Y. A. Bogale, P. Fasouli and M. Salem, "A Comparative Analysis of Deep Learning Models for Malaria Plasmodium Classification," 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS), Nancy, France, 2024, pp. 1-4, doi: 10.1109/ICECS61496.2024.10848723.
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