Automatic Detection of COVID-19 Disease by Using Transfer Learning of Light Weight Deep Learning Model

Abstract

COVID-19 began to appear in China in December 2019 with cases of pneumonia. Controlling the effects of this virus type is closely related to all scientists, especially medical doctors. In this study, ShuffleNet CNN architecture, which is suitable for mobile devices, is one of the deep learning models that perform today's most successful image recognition and classification processes. Obtaining a large data set in the training process is an important problem for the diagnosis of COVID-19 disease. However, rapid diagnosis of COVID-19 X-ray or CT images plays a crucial role in the immediate decision of medical doctors. However, the lack of training data causes poor performance due to the problem of over-fitting. Also, the back propagation algorithm used in CNN training is often very slow and requires the tuning of different hyper parameters. To overcome these disadvantages, this study proposed a new approach based entirely on the machine learning algorithms, extracting CNN features from the light weight ShuffleNet architecture. In this study, a total of 300 images were used, the images diagnosed as COVID-19 and normal chest images. During the classification phase, 10-fold cross validation was applied to the database and 99.98% accuracy rate was obtained.