Using X-ray images and deep learning for automated detection of coronavirus disease

Abstract

Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).

Communicated by Ramaswamy H. Sarma

Keywords:

• Computer-aided diagnosis
• coronavirus automatic detection
• Covid-19
• CT and X-ray images
• pneumonia
• deep learning

Acknowledgements

We thank the reviewer for his/her thorough review and highly appreciate the comments, corrections and suggestions that ensued, which significantly contributed to improving the quality of the publication.

Disclosure statement

We declare that we have no conflicts of interest to disclose.

Funding

Authors have not received research grants from any Company.

Author’s contributions

The experiments and the programming stage were carried out by Khalid El Asnaoui. All authors wrote the paper, and all approve this submission.

Materials

Chest X-ray & CT dataset (Kermany et al., 2018) available at https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

Covid Chest X-ray Dataset (Cohen et al., 2020) available at https://github.com/ieee8023/covid-chestxray-dataset

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.