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

arXiv: https://arxiv.org/abs/2102.06388 · HTML: https://mlsanity.com/paper/2102.06388/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 68 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 19 of 19)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging](https://mlsanity.com/paper/2007.06537/) (2020)
- [Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis](https://mlsanity.com/paper/2003.05037/) (2020)
- [Epileptic Seizures Detection Using Deep Learning Techniques: A Review](https://mlsanity.com/paper/2007.01276/) (2020)
- [Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection](https://mlsanity.com/paper/2003.12338/) (2020)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms](https://mlsanity.com/paper/2004.00038/) (2020)
- [Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection](https://mlsanity.com/paper/2012.11840/) (2020)
- [COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches](https://mlsanity.com/paper/2004.03747/) (2020)
- [CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image](https://mlsanity.com/paper/2005.03059/) (2020)
- [A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan](https://mlsanity.com/paper/2006.14419/) (2020)
- [Exact Inference for Gaussian Process Regression in case of Big Data with the Cartesian Product Structure](https://mlsanity.com/paper/1403.6573/) (2014)
- [Generative adversarial networks for generation and classification of physical rehabilitation movement episodes](https://mlsanity.com/paper/1812.06307/) (2018)

## Built on by (most cited first, 13 of 13)

- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods](https://mlsanity.com/paper/2104.15007/) (2021)
- [SemPPL: Predicting pseudo-labels for better contrastive representations](https://mlsanity.com/paper/2301.05158/) (2023)
- [Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients](https://mlsanity.com/paper/2104.08954/) (2021)
- [Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression](https://mlsanity.com/paper/2205.15858/) (2022)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression](https://mlsanity.com/paper/2109.05461/) (2021)
- [Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray images](https://mlsanity.com/paper/2106.14724/) (2021)
- [What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors](https://mlsanity.com/paper/2109.05457/) (2021)
- [Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit](https://mlsanity.com/paper/2204.05476/) (2022)
- [SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment](https://mlsanity.com/paper/2204.07772/) (2022)

## Abstract (arXiv)

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%.