# Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture Source: [Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture]() Collection index: [llms.txt]() Data updated: 2026-01-07T05:00:04.580Z Authors: \\'Angel Miguel Garc\\'ia-Vico, Huseyin Seker, Muhammad Afzal Published: 2025-12-17T10:25:01.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on Chicken Dataset](): Image Classification; Chicken Dataset; Accuracy: 98.32. - [Classification on Rabbit Dataset](): Classification; Rabbit Dataset; Accuracy: 97.99. ## Original sources and implementation [Original paper]() [PDF]()