A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN

•Application of a deep-learning model, Mask R-CNN trained to identify healthy and Plasmodium-infected red blood cells.•A predictive model with 15 times faster parasite counting efficiency compared to manual methods.•Ability to generate segmentation masks on top of bounding box classifications for immediate visualization and stage-specific identification.•Potential to reduce errors common in manual counting through subsequent standardization. Accurate and early diagnosis is critical to proper malaria treatment and hence death prevention. Several computer vision technologies have emerged in recent years as alternatives to traditional microscopy and rapid diagnostic tests. In this work, we used a deep learning model called Mask R-CNN that is trained on uninfected and Plasmodium falciparum-infected red blood cells. Our predictive model produced reports at a rate 15 times faster than manual counting without compromising on accuracy. Another unique feature of our model is its ability to generate segmentation masks on top of bounding box classifications for immediate visualization, making it superior to existing models. Furthermore, with greater standardization, it holds much potential to reduce errors arising from manual counting and save a significant amount of human resources, time, and cost.