A Two-Step Learning Model for the Diagnosis of Coronavirus Disease-19 Based on Chest X-ray Images with 3D Rotational Augmentation

Abstract

Herein, we propose a method for effectively classifying normal, coronavirus disease-19 (COVID-19), lung opacity, and viral pneumonia symptoms using chest X-ray images. The proposed method comprises a lung detection model, three-dimensional (3D) rotational augmentation, and a two-step learning model. The lung detection model is used to detect the position of the lungs in X-ray images. The lung position detected by the lung detection model is used as the bounding box coordinates of the two-step learning model. The 3D rotational augmentation, which is a data augmentation method based on 3D photo inpainting, solves the imbalance in the amount of data for each class. The two-step learning model is proposed to improve the model performance by first separating the normal cases, which constitute the most data in the X-ray images, from other disease cases. The two-step learning model comprises a two-class model for classifying normal and disease images, as well as a three-class model for classifying COVID-19, lung opacity, and viral pneumonia among the diseases. The proposed method is quantitatively compared with the existing algorithm, and results show that the proposed method is superior to the existing method.