# A Radiomics-Boosted Deep-Learning Model for COVID-19 and Non-COVID-19 Pneumonia Classification Using Chest X-ray Image

arXiv: https://arxiv.org/abs/2107.08667 · HTML: https://mlsanity.com/paper/2107.08667/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2021.

## Built on by (most cited first, 4 of 4)

- [A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation](https://mlsanity.com/paper/2303.10533/) (2023)
- [A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation](https://mlsanity.com/paper/2203.00628/) (2022)
- [Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography](https://mlsanity.com/paper/2504.12249/) (2025)
- [Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification](https://mlsanity.com/paper/2504.10916/) (2025)

## Abstract (arXiv)

To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-learning models were trained based on a pre-trained VGG-16 architecture: in the 1st model, X-ray image was the sole input; in the 2nd model, X-ray image and 2 radiomic feature maps (RFM) selected by the saliency map analysis of the 1st model were stacked as the input. Both models were developed using 812 chest X-ray images with 262/288/262 COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. In 1st model using X-ray as the sole input, the 1) sensitivity, 2) specificity, 3) accuracy, and 4) ROC Area-Under-the-Curve of COVID-19 vs Non-COVID-19 pneumonia detection were 1) 0.90±0.07 vs 0.78±0.09, 2) 0.94±0.04 vs 0.94±0.04, 3) 0.93±0.03 vs 0.89±0.03, and 4) 0.96±0.02 vs 0.92±0.04. In the 2nd model, two RFMs, Entropy and Short-Run-Emphasize, were selected with their highest cross-correlations with the saliency maps of the 1st model. The corresponding results demonstrated significant improvements (p<0.05) of COVID-19 vs Non-COVID-19 pneumonia detection: 1) 0.95±0.04 vs 0.85±0.04, 2) 0.97±0.02 vs 0.96±0.02, 3) 0.97±0.02 vs 0.93±0.02, and 4) 0.99±0.01 vs 0.97±0.02. The reduced variations suggested a superior robustness of 2nd model design.