Malaria is a disease that occurs worldwide, especially in tropical regions where a high prevalence is observed. Difficulties are encountered especially in developing countries where resources in terms of equipment and trained personnel are limited. Until today, microscopic analysis is the standard method for diagnosing Plasmodium falciparum, which is the causative agent of malaria. In this paper, we proposed a malaria detection and diagnosis system using a deep learning technique which is a convolutional neural network called YOLOv5. Model learning was performed using a combination of two given image sources, Delgado Dataset B and Dijkstra Dataset, as a dataset containing thin smear images. We then evaluated the performance of the model by comparing it with other state-of-the-art results on deep learning. We obtained for the detection, a mean Average Precision of 96.71%.

Malaria, Deep Learning, Object detection, YOLOv5