# Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images

arXiv: https://arxiv.org/abs/2008.08496 · HTML: https://mlsanity.com/paper/2008.08496/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2102.09559/) (2021)
- [Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey](https://mlsanity.com/paper/2203.00190/) (2022)
- [A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica](https://mlsanity.com/paper/2107.11696/) (2021)
- [Semi-supervised Learning for COVID-19 Image Classification via ResNet](https://mlsanity.com/paper/2103.06140/) (2021)
- [Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities](https://mlsanity.com/paper/2109.00889/) (2021)
- [A Novel Dataset for Financial Education Text Simplification in Spanish](https://mlsanity.com/paper/2312.09897/) (2023)
- [Improving Semi-supervised Deep Learning by using Automatic Thresholding to Deal with Out of Distribution Data for COVID-19 Detection using Chest X-ray Images](https://mlsanity.com/paper/2211.02142/) (2022)
- [Enhancing Classification with Semi-Supervised Deep Learning Using Distance-Based Sample Weights](https://mlsanity.com/paper/2505.14345/) (2025)
- [Medical Image Classification on Imbalanced Data Using ProGAN and SMA-Optimized ResNet: Application to COVID-19](https://mlsanity.com/paper/2512.24214/) (2025)

## Abstract (arXiv)

The Corona Virus (COVID-19) is an internationalpandemic that has quickly propagated throughout the world. The application of deep learning for image classification of chest X-ray images of Covid-19 patients, could become a novel pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in the context of a new highly infectious disease, the datasets are also highly imbalanced,with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch using a very limited number of labelled observations and highly imbalanced labelled dataset. We propose a simple approach for correcting data imbalance, re-weight each observationin the loss function, giving a higher weight to the observationscorresponding to the under-represented class. For unlabelled observations, we propose the usage of the pseudo and augmentedlabels calculated by MixMatch to choose the appropriate weight. The MixMatch method combined with the proposed pseudo-label based balance correction improved classification accuracy by up to 10%, with respect to the non balanced MixMatch algorithm, with statistical significance. We tested our proposed approach with several available datasets using 10, 15 and 20 labelledobservations. Additionally, a new dataset is included among thetested datasets, composed of chest X-ray images of Costa Rican adult patients