Improvement of Xception-ResNet50V2 Concatenation for COVID-19 Detection on Chest X-Ray Images

Abstract

Novel coronavirus disease (COVID-19) has globally become a pandemic since the first quarter of 2020 whose growth rate must immediately be controlled. One of strategy to reduce the growth rate of sufferers is to break the chain of the spread by detecting it and carrying out quarantine. X-ray imaging can be used as a modality to detect the COVID-19 in suspected patients' lungs as a clinical diagnostic tool. One of the challenges of this task is the difficulty in distinguishing the characteristics of COVID-19 from other diseases with similar features of the images resulted from the X-ray of the chest. To reduce the problems that will be faced, machine learning or deep learning is embedded in an automatic computer-Aided diagnosis (CAD) to increase efficiency and accuracy. Several deep learning-based artificial intelligence systems can be used in diagnosis, one of the most popular is using the previously proposed Convolutional Neural Network (CNN), which has promising accuracy in detecting COVID-19 confirmed patients using CXR images. In this study, to detect COVID-19 confirmed patients by classifying them into 4 classes, we propose a modified combination of two CNN architectures named Xception and ResNet50V2 which makes the system powerful using multiple feature extraction capabilities the proposed method achieves high accuracy, precision, recall, and F1-Score, reaching 93.412%, 96.6%, 99.6%, and 98%, respectively. Overall, the proposed method can be used as an automatic diagnosis system that can be utilized by clinical practitioners and radiologists to diagnose, validate, and follow-up of COVID-19 suspected cases.