COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcription polymerase chain reaction(RT-PCR)test is used to screen for this disease,but its low sensitivity means that it is not sufficient for early detection and treatment.As RT-PCR is a time-consuming procedure,there is interest in the introduction of automated techniques for diagnosis.Deep learning has a key role to play in the field of medical imaging.The most important issue in this area is the choice of key features.Here,we propose a set of deep learning features based on a system for automated classification of computed tomography(CT)images to identify COVID-19.Initially,this method was used to prepare a database of three classes:Pneumonia,COVID19,and Healthy.The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach.In the next step,two advanced deep learning models(ResNet50 and DarkNet53)were fine-tuned and trained through transfer learning.The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach.For each deep model,the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach.Later,the selected features were merged using the new minimum parallel distance non-redundant(PMDNR)approach.The final fused vector was finally classified using the extreme machine classifier.The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%.Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.