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arXiv · 2601.00806

Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture

Ángel Miguel García-Vico·Huseyin Seker·Muhammad Afzal
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Abstract

Coccidiosis, a disease caused by the Eimeria parasite, represents a major threat to the poultry and rabbit industries, demanding rapid and accurate diagnostic tools. While deep learning models offer high precision, their significant energy consumption limits their deployment in resource-constrained environments. This paper introduces a novel two-stage Spiking Neural Network (SNN) architecture, where a pre-trained Convolutional Neural Network is first converted into a spiking feature extractor and then coupled with a lightweight, unsupervised SNN classifier trained with Spike-Timing-Dependent Plasticity (STDP). The proposed model sets a new state-of-the-art, achieving 98.32\% accuracy in Eimeria classification. Remarkably, this performance is accomplished with a significant reduction in energy consumption, showing an improvement of more than 223 times compared to its traditional ANN counterpart. This work demonstrates a powerful synergy between high accuracy and extreme energy efficiency, paving the way for autonomous, low-power diagnostic systems on neuromorphic hardware.

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Ángel Miguel García-Vico, Huseyin Seker, Muhammad Afzal. 2025-12-17. Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture. https://arxiv.org/abs/2601.00806

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