Version 1
: Received: 2 November 2024 / Approved: 4 November 2024 / Online: 5 November 2024 (08:32:45 CET)
How to cite:
Carneiro, G. S.; Xavier, K. C.; Sindeaux-Neto, J. L.; Lima da Silva, A. D. S.; Oliveira da Silva, M. V. MLens: Advancing Real-Time Detection, Identification and Counting of Pathogenic Microparasites through a Web Interface. Preprints2024, 2024110235. https://doi.org/10.20944/preprints202411.0235.v1
Carneiro, G. S.; Xavier, K. C.; Sindeaux-Neto, J. L.; Lima da Silva, A. D. S.; Oliveira da Silva, M. V. MLens: Advancing Real-Time Detection, Identification and Counting of Pathogenic Microparasites through a Web Interface. Preprints 2024, 2024110235. https://doi.org/10.20944/preprints202411.0235.v1
Carneiro, G. S.; Xavier, K. C.; Sindeaux-Neto, J. L.; Lima da Silva, A. D. S.; Oliveira da Silva, M. V. MLens: Advancing Real-Time Detection, Identification and Counting of Pathogenic Microparasites through a Web Interface. Preprints2024, 2024110235. https://doi.org/10.20944/preprints202411.0235.v1
APA Style
Carneiro, G. S., Xavier, K. C., Sindeaux-Neto, J. L., Lima da Silva, A. D. S., & Oliveira da Silva, M. V. (2024). MLens: Advancing Real-Time Detection, Identification and Counting of Pathogenic Microparasites through a Web Interface. Preprints. https://doi.org/10.20944/preprints202411.0235.v1
Chicago/Turabian Style
Carneiro, G. S., Alanna do Socorro Lima da Silva and Michele Velasco Oliveira da Silva. 2024 "MLens: Advancing Real-Time Detection, Identification and Counting of Pathogenic Microparasites through a Web Interface" Preprints. https://doi.org/10.20944/preprints202411.0235.v1
Abstract
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.
Keywords
Aprendizado de máquina; parasitologia; mixozoários; detecção de objetos; microscópio; YOLO
Subject
Biology and Life Sciences, Parasitology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
