Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images

TL;DR

This study develops a method using convolutional neural network features and support vector machines to accurately detect Covid-19 from chest X-ray images, achieving over 99% overall performance.

Contribution

The paper introduces a novel approach combining ResNet-50 features with SVM classifiers for Covid-19 detection in X-ray images, demonstrating high accuracy.

Findings

SVM-quadratic achieved 96.35% sensitivity.

Overall performance exceeded 99% with SVM classifiers.

Method can assist radiologists and reduce false detections.

Abstract

Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid-19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 96.35% with the 5-fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection.