Deep ensemble models with multiscale lung-focused patches for pneumonia classification on chest x-ray

Abstract

Recently, deep learning-based pneumonia classification has shown excellent performance in chest X-ray(CXR) images, but when analyzing classification results through visualization such as Grad-CAM, deep learning models have limitations in classifying by observing the outside of the lungs. To overcome these limitations, we propose a deep ensemble model with multiscale lung-focused patches for the classification of pneumonia. First, Contrast Limited Adaptive Histogram Equalization is applied to appropriately increase the local contrast while maintaining important features. Second, lung segmentation and multiscale lung-focused patches generation is performed to prevent pneumonia diagnosis from external lung region information. Third, we use a classification network with a Convolutional Block Attention Module to make the model to focus on meaningful regions and ensemble single models trained on large, middle and small-sized patches, respectively. For the evaluation of the proposed classification method, the model was trained on 5,216 pediatric CXRs and tested 624 images. Deep ensemble model trained on large and middle-sized patches showed the best performance with an accuracy of 92%, which is a 15%p improvement over the original single model.