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COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images

arXiv 2004.02696 ↗First posted 6 Apr 2020cs.CV cs.LG eess.IVcited by 34

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Abstract (arXiv)

Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To further improve diagnosis capabilities of the COVID-CAPS, pre-training based on a new dataset constructed from an external dataset of X-ray images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2020.

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Citing papers per year: 2021: 22; 2022: 5; 2023: 3; 2024: 2; 2025: 2.

34 papers.

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  1. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  2. 2021COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Imagescited by 22
  3. 2021Self-supervised deep convolutional neural network for chest X-ray classificationcited by 16
  4. 2022Learning with Capsules: A Surveycited by 10
  5. 2021Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learningcited by 8
  6. 2021Distilling EEG Representations via Capsules for Affective Computingcited by 8
  7. 2021UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detectioncited by 8
  8. 2021COVID-19 Modeling: A Reviewcited by 7
  9. 2021The Prominence of Artificial Intelligence in COVID-19cited by 7
  10. 2021Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Datasetcited by 4
  11. 2023COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Imagescited by 4
  12. 2023The Utility of the Virtual Imaging Trials Methodology for Objective Characterization of AI Systems and Training Datacited by 4
22 more
  1. 2021Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Reviewcited by 3
  2. 2021Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentationcited by 3
  3. 2022Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensemblingcited by 3
  4. 2023DINO-CXR: A self supervised method based on vision transformer for chest X-ray classificationcited by 3
  5. 2021Online unsupervised Learning for domain shift in COVID-19 CT scan datasetscited by 2
  6. 2021Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Networkcited by 1
  7. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  8. 2021MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Imagescited by 1
  9. 2021Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the Systemcited by 1
  10. 2021CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Imagescited by 1
  11. 2021Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLPcited by 0
  12. 2021AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Imagescited by 0
  13. 2021WisdomNet: Prognosis of COVID-19 with Slender Prospect of False Negative Cases and Vaticinating the Probability of Maturation to ARDS using Posteroanterior Chest X-Rayscited by 0
  14. 2021COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scanscited by 0
  15. 2021Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNNcited by 0
  16. 2022A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networkscited by 0
  17. 2022Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Imagescited by 0
  18. 2022Affordance detection with Dynamic-Tree Capsule Networkscited by 0
  19. 2024Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Imagescited by 0
  20. 2024From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19cited by 0
  21. 2025Application of discrete Ricci curvature in pruning randomly wired neural networks: A case study with chest x-ray classification of COVID-19cited by 0
  22. 2025PR-CapsNet: Pseudo-Riemannian Capsule Network with Adaptive Curvature Routing for Graph Learningcited by 0