MLens: Advancing 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 indicate that machine learning has the potential to make micro-parasite detection more efficient and less reliant on manual work in parasitology. The beta version of the MLens shows 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 represents a significant ad-vancement, opening new possibilities for the application of machine learning in parasitology and substantially accelerating parasite detection.