A Hybrid Model Based on Deep Features and Ensemble Learning for the Diagnosis of COVID-19: DeepFeat-E

Abstract

COVID-19, which has been declared a pandemic disease, has affected the lives of millions of people and caused a major epidemic. Despite the development of vaccines and vaccination to prevent the transmission of the disease, COVID-19 case rates fluctuate worldwide. Therefore, rapid and reliable diagnosis of COVID-19 disease is of critical importance. For this purpose, a hybrid model based on transfer learning methods and ensemble classifiers is proposed in this study. In this hybrid approach, called DeepFeat-E, the diagnosis process is performed by using deep features obtained from transfer learning models and ensemble classifiers consisting of classical machine learning methods. To test the proposed approach, a dataset of 21,165 X-ray images including 10,192 Normal, 6012 Lung Opacity, 1345 Viral Pneumonia and 3616 COVID-19 were used. With the proposed approach, the highest accuracy was achieved with the deep features of the DenseNet201 transfer learning model and the Stacking ensemble learning method. Accordingly, the test accuracy was 90.17%, 94.99% and 94.93% for four, three and two class applications, respectively. According to the results obtained in this study, it is seen that the proposed hybrid system can be used quickly and reliably in the diagnosis of COVID-19 and lower respiratory tract infections.