COVID-19 classification based on a convolutional spiking neural network: A modified exponential IF neuron approach

Abstract

The many fatalities due to this worldwide epidemic, referred to as COVID-19. A lack of resources has necessitated using scientifically validated procedures like computed tomography scans (CT scans) and chest X-rays to diagnose the illness early on COVID-19 and stop it from spreading across the population. Deep learning models have shown that these processors outperform traditional CPUs and GPUs regarding power and efficiency. Thanks to these processors, Spiking Neural Networks (SNNs) can be implemented more easily in real-world settings. The potential-based features used in the classifier are extracted from a DCSNN, built with a specific structure, and the corresponding proposed ME-IF neuron model. SNN neuron models are based on mathematical representations of biological neurons. Accordingly, have attempted to model SNNs based on a Modified Exponential Integrate-and-Fire (IF) neuron using a variety of deep learning packages. These methods have been used to distinguish between chest CT scan pictures that are COVID and those that are not. The proposed model surpasses several leading approaches with precision and an F1 score of 0.84. The drawback of DCSNN academics from all around the globe is still interested in learning more about how brains operate to develop computer algorithms.

Keywords

• COVID-19
• CT scans
• Computer vision
• Deep learning
• Neuromorphic computing
• Spiking neural networks