Improving the automated estimation of malaria parasite density detection
Institut de Mathématiques et de Sciences Physiques, Dangbo, Bénin, Mouazamou Ibrahim, Université d’Abomey Calavi, Ismaïl Lawani, Faculté des Sciences de la Sante Université d’Abomey Calavi, Cotonou, Bénin, Dagba Theophile, Ecole d’Economie Appliquée et de Management Université d’Abomey Calavi, Cotonou, Bénin, Jules Dégila, Institut de Mathématiques et de Sciences Physiques, Dangbo, Bénin ; Université d’Abomey Calavi, Amadou Habiboulaye, IA4Africa.org, Paris France · 2024
Malaria is an extremely deadly disease caused by the Plasmodium parasite, which is transmitted to humans through the bites of infected mosquitoes. The fight against this disease relies on early and accurate diagnosis. However, existing diagnostic tools have significant limitations, which are aggravated by a growing lack of qualified medical professionals globally, making the management of malaria more complicated than ever. Computer vision techniques have been largely used in this domain to propose approaches for combating malaria. While these approaches offer various advantages, they also present certain limitations. In this article, we have collaborated with malaria diagnostic experts to develop an innovative malaria diagnostic system. This system is based on the Yolov5 object detection model, that we modified to improve the precision of detection of small objects, such as malaria parasites, a particularly complex challenge in the medical field. This approach enabled the simultaneous identification of parasites and white blood cells (WBC), offering a more complete analysis than most previous research. After training, the model achieved 93.2% precision, 96.6% recall, 98.1% mAP50 and 94.86% F1 score for malaria parasite detection. For the WBC detection, performance was even higher, with 99.7% precision, 99.6% recall, 99.5% mAP50 and 99.64% F1 score. We have consequently used this model to develop our precise diagnostic system, which is also accessible to non-experts, enabling us to respond effectively to the growing shortage of qualified medical practitioners. The system has been tested on a set of 19 blood smear slides, and the results confirm that our approach offers a malaria diagnostic precision comparable to that of an expert.