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

TL;DR

This paper introduces a semi-supervised AI method using GANs and edge detection to improve COVID-19 detection from lung scans, especially when labeled data is limited, outperforming traditional supervised models.

Contribution

First semi-supervised COVID-19 detection method utilizing GANs and edge detection, effective with limited labeled data, outperforming supervised CNNs in accuracy and reliability.

Findings

Achieved 99.56% accuracy with semi-supervised method.

Outperformed supervised CNN with scarce labeled data.

Demonstrated robustness with high sensitivity and specificity.

Abstract

The new coronavirus has caused more than one 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. Our motivation is to develop an automatic method that can cope with scenarios in which preparing labeled data is time consuming or expensive. In this article, we propose a Semi-supervised Classification using Limited Labeled Data (SCLLD) relying on Sobel edge detection and Generative Adversarial Networks (GANs) to automate the COVID-19 diagnosis. The GAN discriminator output is a probabilistic value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid Hospital, whereas a public dataset is also used for validating our system. 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 semi-supervised method for COVID-19 detection is presented. Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data. Thus, our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 +- 0.20%, 99.88 +- 0.24%, and 99.40 +- 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 +- 4.11%, 91.2 +- 6.15%, and 46.40 +- 5.21%.