Automated malaria diagnosis and parasitemia estimation using a customized OpenFlexure microscope

We present a customized OpenFlexure Microscope (OFM) platform for automated malaria diagnosis and parasitemia estimation. The system integrates a Laplacian-based autofocusing algorithm optimized for 100× oil immersion imaging and a YOLO-based deep learning model to detect Plasmodium-infected and non-infected red blood cells and white blood cells. A Python control script performs automated slide scanning and real-time analysis across 100 fields of view. The OFM achieved 80% accuracy in parasitemia estimation, compared to 38% for human microscopists, and reduced diagnostic time from over 100 minutes to under 40 minutes, demonstrating its potential for use in point-of-care settings.