A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images

Abstract

Purpose: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID-19 and non-COVID-19 pneumonia detection using chest x-ray images.

Methods: As a novel radiomics approach, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire chest x-ray image; thus, each feature is rendered as a 2D map in the same dimension as the x-ray image. Based on each of the three investigated deep neural network architectures, including VGG-16, VGG-19, and DenseNet-121, a pilot model was trained using x-ray images only. Subsequently, two radiomic feature maps (RFMs) were selected based on cross-correlation analysis in reference to the pilot model saliency map results. The radiomics-boosted model was then trained based on the same deep neural network architecture using x-ray images plus the selected RFMs as input. The proposed radiomics-boosted design was 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. For each model, 50 runs were trained with random assignments of training/validation cases following the 7:1 ratio in the training-validation set. Sensitivity, specificity, accuracy, and ROC curves together with area-under-the-curve (AUC) from all three deep neural network architectures were evaluated.

Results: After radiomics-boosted implementation, all three investigated deep neural network architectures demonstrated improved sensitivity, specificity, accuracy, and ROC AUC results in COVID-19 and healthy individual classifications. VGG-16 showed the largest improvement in COVID-19 classification ROC (AUC from 0.963 to 0.993), and DenseNet-121 showed the largest improvement in healthy individual classification ROC (AUC from 0.962 to 0.989). The reduced variations suggested improved robustness of the model to data partition. For the challenging non-COVID-19 pneumonia classification task, radiomics-boosted implementation of VGG-16 (AUC from 0.918 to 0.969) and VGG-19 (AUC from 0.964 to 0.970) improved ROC results, while DenseNet-121 showed a slight yet insignificant ROC performance reduction (AUC from 0.963 to 0.949). The achieved highest accuracy of COVID-19/non-COVID-19 pneumonia/healthy individual classifications were 0.973 (VGG-19)/0.936 (VGG-19)/ 0.933 (VGG-16), respectively.

Conclusions: The inclusion of radiomic analysis in deep learning model design improved the performance and robustness of COVID-19/non-COVID-19 pneumonia/healthy individual classification, which holds great potential for clinical applications in the COVID-19 pandemic.

Keywords: COVID-19; deep learning; radiomics; x-ray.

FIGURE 1

Diagrams of the three studied deep neural networks. (a) VGG‐16, (b) VGG‐19, and (c) DenseNet‐121

FIGURE 2

A workflow summary of radiomic feature map (RFM) calculation in this work

FIGURE 3

Image comparisons from three example cases for the VGG‐16 pilot model. The GLRLM SRE RFMs and saliency map (overlaid with X‐ray image) are illustrated in 0.3 power scale

FIGURE 4

The ROC results of pilot model versus radiomics‐boosted model using (a) VGG‐16, (b) VGG‐19, and (c) DenseNet‐121 deep neural network architecture. 0.3 power scale was used in the y axis to highlight the difference

FIGURE 5

The SM cross‐correlation matrix of radiomics‐boosted model on test set for left: VGG‐16; middle: VGG‐19; right: DenseNet121 architectures. The x and y axes represent the sample ID in the test set, sorting with the order of healthy/non‐COVID‐19 pneumonia/COVID‐19 cohorts