  The new coronavirus has caused more than 1 million deaths and continues to
spread rapidly. This virus targets the lungs, causing respiratory distress
which can be mild or severe. The X-ray or computed tomography (CT) images of
lungs can reveal whether the patient is infected with COVID-19 or not. Many
researchers are trying to improve COVID-19 detection using artificial
intelligence. In this paper, relying on Generative Adversarial Networks (GAN),
we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD)
for automated COVID-19 detection. Our motivation is to develop learning method
which can cope with scenarios that preparing labelled data is time consuming or
expensive. We further improved the detection accuracy of the proposed method by
applying Sobel edge detection. The GAN discriminator output is a probability
value which is used for classification in this work. The proposed system is
trained using 10,000 CT scans collected from Omid hospital. Also, we validate
our system using the public dataset. The proposed method is compared with other
state of the art supervised methods such as Gaussian processes. To the best of
our knowledge, this is the first time a COVID-19 semi-supervised detection
method is presented. Our method is capable of learning from a mixture of
limited labelled and unlabelled data where supervised learners fail due to lack
of sufficient amount of labelled data. Our semi-supervised training method
significantly outperforms the supervised training of Convolutional Neural
Network (CNN) in case labelled training data is scarce. Our method has achieved
an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where
CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of
94%, and specificity of 46.40%.
