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The consumption of microbial-contaminated food and water is responsible forthe deaths of millions of people annually. Smartphone-based microscopy systemsare portable, low-cost, and more accessible alternatives for the detection ofGiardia and Cryptosporidium than traditional brightfield microscopes. However,the images from smartphone microscopes are noisier and require manual cystidentification by trained technicians, usually unavailable in resource-limitedsettings. Automatic detection of (oo)cysts using deep-learning-based objectdetection could offer a solution for this limitation. We evaluate theperformance of three state-of-the-art object detectors to detect (oo)cysts ofGiardia and Cryptosporidium on a custom dataset that includes both smartphoneand brightfield microscopic images from vegetable samples. Faster RCNN,RetinaNet, and you only look once (YOLOv8s) deep-learning models were employedto explore their efficacy and limitations. Our results show that while thedeep-learning models perform better with the brightfield microscopy imagedataset than the smartphone microscopy image dataset, the smartphone microscopypredictions are still comparable to the prediction performance of non-experts.

Deep-learning assisted detection and quantification of (oo)cysts of Giardia and Cryptosporidium on smartphone microscopy images