MLens: Advancing the Real-Time Detection, Identification, and Counting of Pathogenic Microparasites Through a Web Interface

In this study, a diverse collection of images of myxozoans from the genera Henneguya and Myxobolus was created, providing a practical dataset for application in computer vision. Four versions of the YOLOv5 network were tested, achieving an average precision of 97.9%, a recall of 96.7%, and an F1 score of 97%, demonstrating the effectiveness of MLens in the automatic detection of these parasites. These results indicated that machine learning has the potential to make microparasite detection more efficient and less reliant on manual work in parasitology. The beta version of the MLens showed strong performance, and future improvements may include fine-tuning the WebApp hyperparameters, expanding to other myxosporean genera, and refining the model to handle more complex optical microscopy scenarios. This work presented a significant advancement, opening new possibilities for the application of machine learning in parasitology and substantially accelerating parasite detection.