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

arXiv 2601.00806 ↗First posted 17 Dec 2025cs.NE cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 47 references that are in the map. First posted on arXiv 17 Dec 2025.

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References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  7. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 145
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  9. 2024Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Trainingcited by 59
  10. 2022A Review of Deep Transfer Learning and Recent Advancementscited by 57
  11. 2020Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activationscited by 48
  12. 2024Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methodscited by 25
6 more
  1. 2017BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticitycited by 23
  2. 2021Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Visioncited by 22
  3. 2021A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approachescited by 20
  4. 2016Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnosticscited by 6
  5. 2025Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspectivecited by 3
  6. 2019Minibatch Processing in Spiking Neural Networkscited by 1

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