A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2

• Published in: Informatics in medicine unlocked Vol. 19; p. 100360
• Main Authors: Rahimzadeh, Mohammad, Attar, Abolfazl
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 2020; The Authors. Published by Elsevier Ltd; Elsevier
• Subjects: Chest X-ray images; Convolutional neural networks; Coronavirus; COVID-19; Deep feature extraction; Deep learning; Transfer learning; Deep learning; COVID-19; Coronavirus; Deep feature extraction; Transfer learning; Chest X-ray images; Convolutional neural networks
• ISSN: 2352-9148; 2352-9148
• DOI: 10.1016/j.imu.2020.100360

In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%. •We introduce a deep convolution network based on the concatenation of Xception and ReNet50V2 to improve the accuracy.•We propose a training technique for dealing with unbalanced datasets.•We evaluate our networks on 11302 chest X-ray images.•We have evaluated ResNet50V2 and Xception on our dataset and compared our proposed network with them. .