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

TL;DR

This paper presents a two-stage spiking neural network architecture for detecting Eimeria parasites that achieves high accuracy while drastically reducing energy consumption, suitable for resource-limited diagnostic applications.

Contribution

It introduces a novel hybrid SNN model combining a pre-trained CNN converted into a spiking feature extractor with an unsupervised SNN classifier, achieving state-of-the-art accuracy with minimal energy use.

Findings

Achieved 98.32% classification accuracy.

Reduced energy consumption by over 223 times compared to traditional ANNs.

Demonstrated suitability for low-power neuromorphic hardware.

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.