AUTHOR=Moura Luís Vinícius de , Mattjie Christian , Dartora Caroline Machado , Barros Rodrigo C. , Marques da Silva Ana Maria 

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

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 3 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2021.662343

DOI=10.3389/fdgth.2021.662343

ISSN=2673-253X

ABSTRACT=COVID-19 uses both reverse transcription-polymerase chain reaction (RT-PCR) and chest X-rays for diagnosis. 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 pathologies. 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 2611 COVID-19 chest X-ray images and 2611 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 model performance was achieved with XGBoost classification, with 0.79 of accuracy, 0.78 of sensitivity, and area under the curve of 0.79. SHAP feature importance values showed a predominance of radiomic feature selection in the right lung, leading to the upper lung zone.