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

Recently, deep learning-based pneumonia classification has shown excellent performance on chest X-ray images, but when analyzing classification results through visualization, it has limitations in classifying by observing the outside of the lungs. In this study, we propose a deep ensemble model with multi-scale lung-focused patches for the classification of pneumonia. The proposed method consists of three steps: contrast enhancement, multi-scale lung-focused patches generation, and deep ensemble model with Convolutional Block Attention Module. The model trained on the large and middle-sized patches improved classification performance with an accuracy of 92% and Grad-CAM visualization showed the model focused on the lung region properly.