Deep-learning assisted detection and quantification of (oo)cysts of
Giardia and Cryptosporidium on smartphone microscopy images
- URL: http://arxiv.org/abs/2304.05339v1
- Date: Tue, 11 Apr 2023 16:58:59 GMT
- Title: Deep-learning assisted detection and quantification of (oo)cysts of
Giardia and Cryptosporidium on smartphone microscopy images
- Authors: Suprim Nakarmi, Sanam Pudasaini, Safal Thapaliya, Pratima Upretee,
Retina Shrestha, Basant Giri, Bhanu Bhakta Neupane, and Bishesh Khanal
- Abstract summary: We evaluate the performance of three state-of-the-art object detectors to detect (oo)cysts of Giardia and Cryptosporidium.
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, the smartphone microscopy predictions are still comparable to the prediction performance of non-experts.
- Score: 0.1349100458364391
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 three 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, and you only look once (YOLOv8s) 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.
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