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

arXiv: https://arxiv.org/abs/2004.02696 · HTML: https://mlsanity.com/paper/2004.02696/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 30 of 34)

- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.06640/) (2021)
- [Self-supervised deep convolutional neural network for chest X-ray classification](https://mlsanity.com/paper/2103.03055/) (2021)
- [Learning with Capsules: A Survey](https://mlsanity.com/paper/2206.02664/) (2022)
- [Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learning](https://mlsanity.com/paper/2104.05121/) (2021)
- [Distilling EEG Representations via Capsules for Affective Computing](https://mlsanity.com/paper/2105.00104/) (2021)
- [UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection](https://mlsanity.com/paper/2105.08590/) (2021)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset](https://mlsanity.com/paper/2108.07148/) (2021)
- [COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images](https://mlsanity.com/paper/2301.01679/) (2023)
- [The Utility of the Virtual Imaging Trials Methodology for Objective Characterization of AI Systems and Training Data](https://mlsanity.com/paper/2308.09730/) (2023)
- [Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2107.09602/) (2021)
- [Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation](https://mlsanity.com/paper/2112.02478/) (2021)
- [Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling](https://mlsanity.com/paper/2212.09281/) (2022)
- [DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification](https://mlsanity.com/paper/2308.00475/) (2023)
- [Online unsupervised Learning for domain shift in COVID-19 CT scan datasets](https://mlsanity.com/paper/2108.02002/) (2021)
- [Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network](https://mlsanity.com/paper/2102.01147/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images](https://mlsanity.com/paper/2108.08775/) (2021)
- [Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System](https://mlsanity.com/paper/2109.12453/) (2021)
- [CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images](https://mlsanity.com/paper/2110.10813/) (2021)
- [Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP](https://mlsanity.com/paper/2103.09904/) (2021)
- [AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images](https://mlsanity.com/paper/2105.10239/) (2021)
- [WisdomNet: Prognosis of COVID-19 with Slender Prospect of False Negative Cases and Vaticinating the Probability of Maturation to ARDS using Posteroanterior Chest X-Rays](https://mlsanity.com/paper/2107.01392/) (2021)
- [COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans](https://mlsanity.com/paper/2107.01527/) (2021)
- [Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNN](https://mlsanity.com/paper/2109.02103/) (2021)
- [A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks](https://mlsanity.com/paper/2201.09952/) (2022)
- [Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images](https://mlsanity.com/paper/2208.01637/) (2022)
- [Affordance detection with Dynamic-Tree Capsule Networks](https://mlsanity.com/paper/2211.05200/) (2022)

## 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%.