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.

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

“…Kurtosis is one of first-order features that is used to compute of the “peakedness” of the distribution of the values. Luís et al [ 43 ] confirmed that COVID-19 induces consolidation and ground glass opacification, resulting in lower kurtosis values and flatter peak.…”

Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics

“…Luís et al . [40] confirmed that COVID-19 induces consolidation and ground glass opacification, resulting in lower kurtosis values and flatter peak.…”

“…Moura et al . [40] observed more heterogeneity of GLRLM in the upper left region of COVID-19 class which may be due to consolidation that tended to diffuse. Pizzi et al .…”

“…Moura et al [40] observed more heterogeneity of GLRLM in the upper left region of COVID-19 class which may be due to consolidation that tended to diffuse. Pizzi et al [37] predicted that the increasing homogeneity in COVID-19 might correlate with the degree of inflammatory infiltrate in the early stage of diffuse alveolar damage.…”

“…Kurtosis is one of first-order features, that is used to compute of the "peakedness" of the distribution of the values. Luís et al [40] confirmed that COVID-19 induces consolidation and ground glass opacification, resulting in lower kurtosis values and flatter peak. A gray level co-occurrence matrix (GLCM) provides the arrangement of voxel pairs to determine texture such as homogeneity (a reflection of the uniformity) of the distribution of voxels.…”