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.
Highlights
Computer-aided diagnosis (CAD) has become an integral part of radiology and clinical diagnosis since the past decades when the emergence of sophisticated imaging techniques, like X-ray, ultrasound, and MRI, became evident
The process of medical diagnosis requires manual observations based on domain knowledge; owing to technological advancement, newer approaches that employ computer-aided practices have been incorporated with the use of artificial intelligence and computer vision techniques significantly gaining grounds [3]
The need for integrating computer algorithms and models with medical diagnosis is made all the more necessary with the global pandemic caused by the novel coronavirus SARSCoV-2 or coronavirus disease 2019 (COVID-19), as named by the World Health Organization (WHO) [4]
Summary
Computer-aided diagnosis (CAD) has become an integral part of radiology and clinical diagnosis since the past decades when the emergence of sophisticated imaging techniques, like X-ray, ultrasound, and MRI, became evident. In the more successful deep learning supervised framework where the model is trained to map the relationship of features to a label, it is challenging to get as many annotated or labeled training samples [9], especially with novel diseases such as COVID19. For this reason, in this research, we experiment the possibility of an automatic detection of COVID-19 using multiple deep learning techniques on available volumes of lung CT scans. Unlike the traditional GAN, we train a generator, supervised discriminator, and unsupervised discriminator model simultaneously
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