Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID-19 in CT Images. (16th February 2021)

• Record Type: Journal Article
• Authors: Oluwasanmi, Ariyo; Aftab, Muhammad Umar; Qin, Zhiguang; Ngo, Son Tung; Doan, Thang Van; Nguyen, Son Ba; Nguyen, Son Hoang
• Other Names: Selisteanu Dan Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-02-16
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/6680455
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The ongoing coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography (CT) scans of the lungs. The complexity and time limitation of the reverse transcription-polymerase chain reaction (RT-PCR) swab test makes it disadvantageous to depend solely on as COVID-19's central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data.