Covid-19 Disease Diagnosis from Radiology Data with Deep Learning Algorithms - Mertyuz, Irem; Mertyuz, Tolga; Tasar, Beyda; Yakut, Oguz (Institute of Electrical and Electronics Engineers Inc., 2020)
Since December 2019, the COVID-19 coronavirus epidemic has spread around the world. In the face of this epidemic, it is important to speed up the diagnosis of the disease in order to reduce the loss of life and to prevent the epidemic from spreading. One of the methods used to diagnose disease is to analyze computed tomography images. In this study, a deep learning algorithm has been developed for disease diagnosis by analysis of computed tomography images. A data set consisting of computerized images of patients with COVID-19, Normal and Pneumonia was used. The results of algorithms with three different network structures are compared. © 2020 IEEE.
4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- Istanbul -- 165025
Keywords: COVID-19 Disease Diagnosis; Deep Learning; GoogleNet; ResNet; VGG-16