mlsanityData to 7 Oct 2026

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

arXiv 2102.06388 ↗First posted 12 Feb 2021eess.IV cs.CVcited by 13

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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%.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 19 of its 68 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

19 of 68 references.

References · 19

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
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  4. 2020COVID-CT-Dataset: A CT Scan Dataset about COVID-19cited by 93
  5. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
  6. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  7. 2020COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Imagescited by 60
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  9. 2020Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysiscited by 38
  10. 2020Epileptic Seizures Detection Using Deep Learning Techniques: A Reviewcited by 32
  11. 2020Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detectioncited by 28
  12. 2020Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Reviewcited by 26
7 more
  1. 2020Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithmscited by 24
  2. 2020Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detectioncited by 20
  3. 2020COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approachescited by 14
  4. 2020CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Imagecited by 8
  5. 2020A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scancited by 5
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  7. 2018Generative adversarial networks for generation and classification of physical rehabilitation movement episodescited by 2

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Citing papers per year: 2021: 9; 2022: 3; 2023: 1.

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  1. 2021Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray imagescited by 10
  2. 2021Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropiescited by 9
  3. 2021Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learningcited by 7
  4. 2021Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methodscited by 6
  5. 2023SemPPL: Predicting pseudo-labels for better contrastive representationscited by 6
  6. 2021Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patientscited by 5
  7. 2022Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regressioncited by 4
  8. 2021An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future workscited by 3
  9. 2021Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regressioncited by 1
  10. 2021Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray imagescited by 0
  11. 2021What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectorscited by 0
  12. 2022Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unitcited by 0
1 more
  1. 2022SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environmentcited by 0