ConvCoroNet: a deep convolutional neural network optimized with iterative thresholding algorithm for Covid-19 detection using chest X-ray images

Abstract

Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly the infected patients. During the peak of this epidemic, there was an insufficiency of Covid-19 test kits. In addition, this technique takes a considerable time in the diagnosis. Hence the need to find fast, accurate and low-cost method to replace or supplement RT PCR-based methods. Covid-19 is a respiratory disease, chest X-ray images are often used to diagnose pneumonia. From this perspective, these images can play an important role in the Covid-19 detection. In this article, we propose ConvCoroNet, a deep convolutional neural network model optimized with new method based on iterative thresholding algorithm to detect coronavirus automatically from chest X-ray images. ConvCoroNet is trained on a dataset prepared by collecting chest X-ray images of Covid-19, pneumonia and normal cases from publically datasets. The experimental results of our proposed model show a high accuracy of 99.50%, sensitivity of 98.80% and specificity of 99.85% when detecting Covid-19 from chest X-ray images. ConvCoroNet achieves promising results in the automatic detection of Covid-19 from chest X-ray images. It may be able to help radiologists in the Covid-19 detection by reducing the examination time of X-ray images.

Communicated by Ramaswamy H. Sarma

Keywords:

• ConvCoroNet
• covid-19
• deep learning
• convolutional neural network (CNNs)
• iterative thresholding algorithm based optimizer
• support vector machine (SVM)
• X-ray chest images

Disclosure statement

The authors have no disclosure statement to disclose.

Data availability statement

Source codes and models used to support the findings of this study are available at GitHub address: https://github.com/merrouchi37/ConvCoroNet.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.