Spiking neural network classification of X-ray chest images

Abstract

Spiking Neural Networks (SNNs) are powerful and biologically plausible models of neural processing and represent a transition to a new generation of neural networks, as they address the problem of high resource requirements by significantly reducing energy consumption. In this paper we investigate the use of SNNs for the diagnosis of COVID-19 cases from chest x-rays, by proposing a simple Spiking Neural Network (SNN) that proves to be effective despite the low resources requested with respect to other solutions proposed in the literature. The paper explains the architecture of the SNN and evaluates the performance of the model in terms of both result accuracy and energy consumption. Experimental results show competitive performance in terms of accuracy and a significant reduction in energy consumption.