Detection of COVID-19 from CT scan images: A spiking neural network-based approach

Abstract

The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT

“…Finally, Garain et. al used a spiking neural network based approach for classification, attaining a F1 score of 0.72 and precision of 0.63 ( Garain et al, 2021 ).…”

Binary Simulated Normal Distribution Optimizer for feature selection: Theory and application in COVID-19 datasets

“…Our normal day-to-day life is destroyed because of this uncertainty. Many works have been proposed for detection of COVID, few of them are Das et al, 2021 , Garain et al, 2021 , Karbhari et al, 2021 etc. We have performed FS on a publicly available COVID-19 dataset for classification purpose of COVID-19.…”

Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models

“…They also tested the application of DenseNet to distinguish COVID-19 positive cases from the negatives with an accuracy of 84.7%. Reference [40] developed a spiking neural network (SSN) by copying biological models, achieving a high F 1 score. Chattopadhyay et al [41] proposed a computationally economical method in which they extracted features of the CT scans, optimizing them by a clustering-based golden ratio optimizer (CGRO), and attained state-of-the-art accuracies on publicly available datasets.…”