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Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images by Sobhan Sheykhivand; Zohreh Mousavi; Sina Mojtahedi; Tohid Yousefi Rezaii; Ali Farzamnia; Saeed Meshgini; Ismail Saad is a Engineering article available to read on EtoBox.
What is Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images about?
The novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a new method for the automatic identification of pneumonia (including COVID-19) is presented using a proposed deep neural network. In the proposed method, the chest X-ray images are used to separate 2-4 classes in 7 different and functional scenarios according to healthy, viral, bacterial, and COVID-19 classes. In the proposed architecture, Generative Adversarial Networks (GANs) are used together with a fusion of the deep transfer learning and LSTM networks, without involving feature extraction/selection for classification of pneumonia. We have achieved more than 90% accuracy for all scenarios except one and also achieved 99% accuracy for separating COVID-19 from healthy group. We also compared our deep proposed network with other deep transfer learning networks (including Inception-ResNet V2, Inception V4, VGG16 and MobileNet) that have been recently widely used in pneumonia detection st
Who reads Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Sobhan Sheykhivand; Zohreh Mousavi; Sina Mojtahedi; Tohid Yousefi Rezaii; Ali Farzamnia; Saeed Meshgini; Ismail Saad
- Publisher
- Elsevier BV
- Published
- 2021
- Language
- EN
- Field
- Engineering (Physical Sciences)