  The consumption of microbial-contaminated food and water is responsible for
the deaths of millions of people annually. Smartphone-based microscopy systems
are portable, low-cost, and more accessible alternatives for the detection of
Giardia and Cryptosporidium than traditional brightfield microscopes. However,
the images from smartphone microscopes are noisier and require manual cyst
identification by trained technicians, usually unavailable in resource-limited
settings. Automatic detection of (oo)cysts using deep-learning-based object
detection could offer a solution for this limitation. We evaluate the
performance of four state-of-the-art object detectors to detect (oo)cysts of
Giardia and Cryptosporidium on a custom dataset that includes both smartphone
and brightfield microscopic images from vegetable samples. Faster RCNN,
RetinaNet, You Only Look Once (YOLOv8s), and Deformable Detection Transformer
(Deformable DETR) deep-learning models were employed to explore their efficacy
and limitations. Our results show that while the deep-learning models perform
better with the brightfield microscopy image dataset than the smartphone
microscopy image dataset, the smartphone microscopy predictions are still
comparable to the prediction performance of non-experts. Also, we publicly
release brightfield and smartphone microscopy datasets with the benchmark
results for the detection of Giardia and Cryptosporidium, independently
captured on reference (or standard lab setting) and vegetable samples. Our code
and dataset are available at
https://github.com/naamiinepal/smartphone_microscopy and
https://doi.org/10.5281/zenodo.7813183, respectively.
