Interpretable multi-stream ensemble learning for radiographic pattern recognition

ABSTRACT Chest radiography (CXR) remains an essential component to evaluate lung diseases. However, it is crucial nowadays to include computer-based tools to aid physicians in the early detection of chest abnormalities. Therefore, this work proposed deep ensemble models to improve the CXR evaluation, interpretability, and reproducibility. Five convolutional neural networks and six different processed image inputs yielded an AUC of 0.982. Furthermore, ensemble learning could produce more reliable outcomes as it did not consider the information of only one method. Moreover, the ensemble strategy balanced the most critical factors from each model to perform a more consistent classification. Finally, class activation and gradient propagation maps allowed locally visualizing CXR regions that most activate neurons from the trained models and explaining practically which areas of the CXR correlated to the model output.