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The new coronavirus has caused more than one million deaths and continues tospread rapidly. This virus targets the lungs, causing respiratory distresswhich can be mild or severe. The X-ray or computed tomography (CT) images oflungs can reveal whether the patient is infected with COVID-19 or not. Manyresearchers are trying to improve COVID-19 detection using artificialintelligence. Our motivation is to develop an automatic method that can copewith scenarios in which preparing labeled data is time consuming or expensive.In this article, we propose a Semi-supervised Classification using LimitedLabeled Data (SCLLD) relying on Sobel edge detection and Generative AdversarialNetworks (GANs) to automate the COVID-19 diagnosis. The GAN discriminatoroutput is a probabilistic value which is used for classification in this work.The proposed system is trained using 10,000 CT scans collected from OmidHospital, whereas a public dataset is also used for validating our system. Theproposed method is compared with other state-of-the-art supervised methods suchas Gaussian processes. To the best of our knowledge, this is the first time asemi-supervised method for COVID-19 detection is presented. Our system iscapable of learning from a mixture of limited labeled and unlabeled data wheresupervised learners fail due to a lack of sufficient amount of labeled data.Thus, our semi-supervised training method significantly outperforms thesupervised training of Convolutional Neural Network (CNN) when labeled trainingdata is scarce. The 95% confidence intervals for our method in terms ofaccuracy, sensitivity, and specificity are 99.56 +- 0.20%, 99.88 +- 0.24%, and99.40 +- 0.18%, respectively, whereas intervals for the CNN (trainedsupervised) are 68.34 +- 4.11%, 91.2 +- 6.15%, and 46.40 +- 5.21%.

Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data