Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography

Abstract

Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

Keywords: SHAP; X-rays; coronavirus; explainable models; machine learning; radiological findings; radiomics.

Figure 1

Examples of CXR images from COVID-19 and non-COVID-19 datasets.

Figure 2

Workflow of the segmentation process.

Figure 3

Feature importance for each radiomic feature with the XGBoost model.

Figure 4

Impact of important features in the XGBoost model output.

Figure 5

Example of SHAP values affecting XGBoost model output for a single COVID-19 CXR image.

Figure 6

Example of SHAP values affecting XGBoost model output for a single non-COVID-19 CXR image.