A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-ray Medical Images
Channabasava Chola, Pramodha Mallikarjuna, Abdullah Y. Muaad, J. V. Bibal Benifa, J. Hanumanthappa, Mugahed A. Al–antari
Abstract
The COVID-19 pandemic has been a global health problem since December 2019. To date, the total number of confirmed cases, recoveries, and deaths has exponentially increased on a daily basis worldwide. In this paper, a hybrid deep learning approach is proposed to directly classify the COVID-19 disease from both chest X-ray (CXR) and CT images. Two AI-based deep learning models, namely ResNet50 and EfficientNetB0, are adopted and trained using both chest X-ray and CT images. The public datasets, consisting of 7863 and 2613 chest X-ray and CT images, are respectively used to deploy, train, and evaluate the proposed deep learning models. The deep learning model of EfficientNetB0 consistently performed a better classification result, achieving overall diagnosis accuracies of 99.36% and 99.23% using CXR and CT images, respectively. For the hybrid AI-based model, the overall classification accuracy of 99.58% is achieved. The proposed hybrid deep learning system seems to be trustworthy and reliable for assisting health care systems, patients, and physicians.