A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-Ray Medical Images

COVID-19 pandemic is a global health problem since December 2019. Up to date, the total number of confirmed, recovered and deaths has exponentially increased on 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 consist of 7,863 and 2,613 chest X-ray and CT images are respectively used to deploy, train, and evaluate the proposed deep learning models. The deep learning model of EfficientNet always 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 trust worth and reliable for assisting health care systems, patients, and physicians.