# COVID-19 Severity Classification on Chest X-ray Images

arXiv: https://arxiv.org/abs/2205.12705 · HTML: https://mlsanity.com/paper/2205.12705/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 13 of 13)

- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection](https://mlsanity.com/paper/2003.10769/) (2020)
- [Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection](https://mlsanity.com/paper/2003.12338/) (2020)
- [Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset](https://mlsanity.com/paper/2004.02060/) (2020)
- [Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images](https://mlsanity.com/paper/2005.03832/) (2020)
- [Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images](https://mlsanity.com/paper/2003.11988/) (2020)
- [Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases](https://mlsanity.com/paper/2105.03266/) (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)

## Abstract (arXiv)

Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite valuable in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on the severity of the infection. First, we pre-process the X-ray images using a median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), and F1-Score (0.92), and precision (0.91).