Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

Abstract The new virus disease (COVID-19) first came to China towards the end of December 2019 and became a pandemic all over the world. The disease caused a large number of people to be infected and die. Rapid diagnosis of the disease is of great importance in controlling transmission. A computed Tomography device provides successful results in the diagnosis of COVID-19 disease. In this study, two-class (COVID-19 and normal) data sets were created from 7200 lung Computed Tomography images diagnosed between March 2020 and November 2020 in a private hospital with the help of specialist physicians. Verification and testing processes were carried out on Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) algorithms from Machine Learning algorithms, and ResNet-50, DenseNet-201, InceptionResNetV2, Inceptionv3, VGG-16, Xception architectures from Deep Learning models. As a result of the studies, the DenseNet-201 architecture obtained the highest result from deep learning models with %99,35 training and test %98,75 accuracy rates, respectively. ANN %97,6, KNN %97,4 and SVM %96,9 accuracy rates were obtained from machine learning.