ABSTRACT
Here, a machine learning tool (YOLOv5) enables the detection of Cryptosporidium microorganisms using optical and phase contrast microscope images. The two databases were processed using 520 images (optical microscopy) and 1200 images (phase contrast microscopy). It used Python libraries to label, standardize the size, and crop the images to generate the input tensors to the YOLOv5 network (s, m, and l). It implemented two experiments using randomly initialized weights in optical and phase contrast microscope images. The other two experiments used the parameters for the best training time obtained before and after retraining the models. Metrics used to assess model accuracy were mean average accuracy, confusion matrix, and the F1 scores. All three metrics confirmed that the optimal model used the best epoch of optical imaging training and retraining with phase contrast imaging. Experiments with randomly initialized weights with optical imaging showed the lowest precision for Cryptosporidium detection. The most stable model was YOLOv5m, with the best results in all categories. However, the differences between all models are lower than 2%, and YOLOv5s is the best option for Cryptosporidium detection considering the differences in computational costs of the models.
