Interpretable Deep Learning Model For Cardiomegaly Detection with Chest X-ray Images

Abstract

Cardiomegaly is a medical disorder characterized by an enlargement of the heart. Many works propose to automatically detect cardiomegaly through chest X-rays. However, most of them are based on deep learning models, known for their lack of interpretability. This work propose a deep learning model for the detection of cardiomegaly based on chest x-rays images and the qualitative assessment of three known local explainable methods, i.e., Grad-CAM, LIME and SHAP. Our model achieved Acc, Prec, Se, Spe, F1-score and AUROC of 91.8±0.7%, 74.0±2.7%, 87.0±5.5%, 92.9±1.2%, 79.8±1.9%, and 90.0±0.7%, respectively. Moreover, except for the SHAP method, our interpretable methods were able to pinpoint the expected location for cardiomegaly. However, Grad-CAM method showed faster computational time than LIME and SHAP.