COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images

TL;DR

This paper presents a hybrid deep learning and feature selection approach for accurate COVID-19 detection from X-ray images, outperforming recent methods.

Contribution

It introduces a novel combination of deep CNN feature extraction, binary differential meta-heuristic feature selection, and SVM classification for COVID-19 detection.

Findings

Achieved 99.43% accuracy in COVID-19 detection.

Demonstrated superior performance over recent studies.

Effective differentiation among COVID-19, pneumonia, and healthy cases.

Abstract

The new Coronavirus is spreading rapidly, and it has taken the lives of many people so far. The virus has destructive effects on the human lung, and early detection is very important. Deep Convolution neural networks are such powerful tools in classifying images. Therefore, in this paper, a hybrid approach based on a deep network is presented. Feature vectors were extracted by applying a deep convolution neural network on the images, and useful features were selected by the binary differential meta-heuristic algorithm. These optimized features were given to the SVM classifier. A database consisting of three categories of images such as COVID-19, pneumonia, and healthy included in 1092 X-ray samples was considered. The proposed method achieved an accuracy of 99.43%, a sensitivity of 99.16%, and a specificity of 99.57%. Our results demonstrate that the suggested approach is better than recent studies on COVID-19 detection with X-ray images.