Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks
Yakup Kutlu, Yunus CAMGÖZLÜ
Abstract
COVID-19 is an epidemic disease that seriously affects elderly people and patients with chronic diseases and causes deaths. Fast and accurate early diagnosis has an important role. Although chest images obtained by computed tomography are accepted as a gold standard, problems are often encountered in accessing this device. For this reason, it is very important to diagnose with more accessible devices such as x-ray machines. These studies have been accelerated with deep neural network models and good results have been obtained. In this study, two different approach models are proposed for this purpose. At first study, training with the COVID-19 data set shared as open access and the test results with different classifiers. The other is the comparison of the results using a Pre-trained model MobileNet. COVID-19 patients, pneumonia patients and normal individuals were classified with 99.53% accuracy by the designed CNN with SVM model which was trained with the COVID-19 data set. As a result, because X-rays are a special type of image, a CNN model trained with X-ray images would be a good choice rather than using pre-trained deep networks with different images. As a result, since X-rays are a special type of picture, it was seen that a CNN model trained with X-ray images should be a better choice, rather than using pre-trained deep networks with different images.