DGCNN: deep convolutional generative adversarial network based convolutional neural network for diagnosis of COVID-19

Abstract

The latest threat to global health is the coronavirus disease 2019 (COVID-19) pandemic. To prevent COVID-19, recognizing and isolating the infected patients is an essential step. The primary diagnosis method is Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, the sensitivity of this test is not satisfactory to successfully control the COVID-19 outbreak. Although there exist many datasets of chest X-rays (CXR) images, but few COVID-19 CXRs are presently accessible owing to privacy of patients. Thus, many researchers have utilized data augmentation techniques to augment the datasets. But, it may cause over-fitting issues, as the existing data augmentation techniques include small modifications to CXRs. Therefore, in this paper, an efficient deep convolutional generative adversarial network and convolutional neural network (DGCNN) is designed to diagnose COVID-19 suspected subjects. Deep convolutional generative adversarial network (DGAN) consists of two networks trained adversarially such that one generates fake images and the other differentiates between them. Thereafter, convolutional neural network (CNN) is utilized for classification purpose. Extensive experiments are conducted to evaluate the performance of the proposed DGCNN. Performance analysis demonstrates that DGCNN can highly improves the diagnosis performance.

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“…The primary objective of GAN is to render the discriminator network incapable of differentiating between the output data generated by the generator network and real data ( Figure 2C ). More recently, deep convolutional GANs (DCGANs) have emerged by combining CNNs and GANs to achieve better performance and effectiveness, resulting in their increasing popularity for designing various computer-aided diagnosis (CADx) models ( 53 , 74 – 76 ).…”