Abstract
The detection of bacterial and viral microbes is pivotal for both human and animal well-being in public health services and veterinary care, but it traditionally requires time-consuming procedures and expert technicians. However, the rise of Machine Learning and Deep Learning has led to a surge in the application of new techniques that can perform bacterial and viral detection faster and at a lower cost. Yet, despite that success, Deep Learning approaches tend to have high energy demands, which can in some contexts limit their application, increasing both costs and environmental concerns. In this study, a new hybrid methodology, in which an Artificial Neural Network was combined with a more energy efficient Spiking Neural Network, was employed to develop a model able to classify 18 species of Eimeria parasites, affecting both rabbits and chickens, from microscope images. We show how significant energy savings can be obtained from SNN layers, while their use in the model can improve its performance.
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This research has been founded from the Recovery, Resilience and Transformation Plan under the project CICERO: CDTI Project CER-20231019 (CICERO).
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Vázquez, I.X., García-Vico, A.M., Seker, H., Sedano, J. (2025). Low Consumption Models for Disease Diagnosis in Isolated Farms. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15346. Springer, Cham. https://doi.org/10.1007/978-3-031-77731-8_22
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