عنوان مقاله [English]
Coronavirus disease 2019 (Coronavirus disease 2019) or Covid-19 (Covid-19) is an infectious disease caused by the acute respiratory syndrome virus (SARS-Cov-2) and has spread worldwide as an epidemic. After the rapid spread of the disease in 2019, the World Health Organization declared a public health emergency; and Human society saw a massive increase in deaths caused by its various mutations. Clinical symptoms include fever, cough, shortness of breath, and loss of smell. Fortunately, recently researchers have been able to achieve many successes in preventing the spread and speeding up its treatment by using different diagnostic methods. The aim of this research is to diagnose the disease of covid-19 by processing CT scan images of people's lungs using ensemble deep learning techniques based on convolutional neural networks (CNN). In this regard, two data sets of CT scan images of people's lungs obtained from Kaggle and GitHub are used. The convolutional neural network architectures used in this research include VGG16, VGG19, Inception v3, ResNet50, DenseNet169, and CtNet10. In the first step, we added two dense (fully connected) layers to each of these models and evaluated their effect. Afterward, to achieve higher accuracy and efficiency, the proposed ensemble method, which is a combination of VGG16, DenseNet169, and ResNet50 architectures, has been used. The experimental results show that the proposed ensemble method is able to achieve an accuracy of 98% to 100% on the investigated data set and significantly improve the performance of deep neural networks in two or multi-classification prediction tasks.