{"ID":2827966,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00806","arxiv_id":"2601.00806","title":"Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture","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.","short_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 p...","url_abs":"https://arxiv.org/abs/2601.00806","url_pdf":"https://arxiv.org/pdf/2601.00806v1","authors":"[\"Ángel Miguel García-Vico\",\"Huseyin Seker\",\"Muhammad Afzal\"]","published":"2025-12-17T10:25:01Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.LG\"]","methods":"[]","has_code":false}