Automated Detection of Malaria (Plasmodium) Parasites in Images Captured with Mobile Phones Using Convolutional Neural Networks

Microscopic examination of Giemsa-stained thick blood smears remains the reference standard for malaria diagnosis, but it requires specialized personnel and is difficult to scale in resource-limited settings. We present a lightweight, smartphone-based system for automatic detection of Plasmodium parasites in thick smears captured with mobile phones attached to a conventional microscope. We built a clinically validated dataset of 400 slides from Loreto, Peru, consisting of 8625 images acquired with three smartphone models and 54,531 annotated instances of Plasmodium vivax and P. falciparum across eight morphologic classes. The workflow includes YOLOv11n-based visual-field segmentation, rescaling, tiling into 640 × 640 patches, data augmentation, and parasite detection. Four lightweight detectors were evaluated; YOLOv11n achieved the best trade-off, with an F1-score of 0.938 and an overall accuracy of 90.92% on the test subset. For diagnostic interpretability, performance was also assessed at the visual-field level by grouping detections into Vivax, Falciparum, Mixed, and Background. On a high-end smartphone (Samsung Galaxy S24 Ultra), the deployed YOLOv11n model achieved 110.9 ms latency per 640 × 640 inference (9.02 FPS).