# COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach

arXiv: https://arxiv.org/abs/2102.06285 · HTML: https://mlsanity.com/paper/2102.06285/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 17 of 17)

- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection](https://mlsanity.com/paper/2003.10769/) (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)
- [An Overview of Deep Learning Architectures in Few-Shot Learning Domain](https://mlsanity.com/paper/2008.06365/) (2020)
- [COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning](https://mlsanity.com/paper/2009.14623/) (2020)
- [Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning](https://mlsanity.com/paper/2011.05317/) (2020)
- [Unsupervised Learning of Local Discriminative Representation for Medical Images](https://mlsanity.com/paper/2012.09333/) (2020)
- [A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial](https://mlsanity.com/paper/2006.01263/) (2020)
- [Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions](https://mlsanity.com/paper/1910.09798/) (2019)
- [An encoder-decoder-based method for COVID-19 lung infection segmentation](https://mlsanity.com/paper/2007.00861/) (2020)
- [A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis](https://mlsanity.com/paper/2101.05410/) (2021)

## Built on by (most cited first, 8 of 8)

- [Making Your First Choice: To Address Cold Start Problem in Vision Active Learning](https://mlsanity.com/paper/2210.02442/) (2022)
- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (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)
- [MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation](https://mlsanity.com/paper/2109.09734/) (2021)
- [Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem](https://mlsanity.com/paper/2301.08441/) (2023)
- [CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19](https://mlsanity.com/paper/2302.09004/) (2023)
- [Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19](https://mlsanity.com/paper/2311.06258/) (2023)
- [Cold-Start Active Preference Learning in Socio-Economic Domains](https://mlsanity.com/paper/2508.05090/) (2025)

## Abstract (arXiv)

In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate. The open-source community collectively has made efforts to collect and annotate the data, but it is not enough to train an accurate deep learning model. Few-shot learning is a sub-field of machine learning that aims to learn the objective with less amount of data. In this work, we have experimented with well-known solutions for data scarcity in deep learning to detect COVID-19. These include data augmentation, transfer learning, and few-shot learning, and unsupervised learning. We have also proposed a custom few-shot learning approach to detect COVID-19 using siamese networks. Our experimental results showcased that we can implement an efficient and accurate deep learning model for COVID-19 detection by adopting the few-shot learning approaches even with less amount of data. Using our proposed approach we were able to achieve 96.4% accuracy an improvement from 83% using baseline models.