# Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods

arXiv: https://arxiv.org/abs/2006.00730 · HTML: https://mlsanity.com/paper/2006.00730/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

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

- [RadVLM: A Multitask Conversational Vision-Language Model for Radiology](https://mlsanity.com/paper/2502.03333/) (2025)
- [Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings](https://mlsanity.com/paper/2104.04518/) (2021)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance](https://mlsanity.com/paper/2301.08479/) (2023)
- [GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification](https://mlsanity.com/paper/2405.20650/) (2024)
- [Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks](https://mlsanity.com/paper/2105.09720/) (2021)
- [Optimizing Data Augmentation Policy Through Random Unidimensional Search](https://mlsanity.com/paper/2106.08756/) (2021)
- [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)
- [A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images](https://mlsanity.com/paper/2501.17160/) (2025)

## Abstract (arXiv)

Purpose: This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. Materials and Methods: From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were compared with the VGG16-based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three-category accuracy was evaluated for test set with 125 CXR images. Results: The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity for COVID-19 pneumonia was more than 90%. The combination of conventional method and mixup was more useful than single type or no data augmentation method. Conclusion: This study was able to create an accurate CADx system for the 3-category classification. Source code of our CADx system is available as open source for COVID-19 research.