Performance Evaluation of Transfer Learning Technique for Automatic Detection of Patients with COVID-19 on X-Ray Images
Oussama El Gannour, Soufiane Hamida, Bouchaib Cherradi, Abdelhadi Raihani, Hicham Moujahid
Abstract
A new pandemic of coronavirus (COVID19) reported for the first time in Wuhan, China. This new virus has spread rapidly around the world with fever, cough, and difficulty breathing symptoms. In this paper, we propose a Deep Learning based system for the diagnosis of COVID19 disease. This system is based on Transfer Learning technique of six pretrained models. The X-Ray image dataset used contains 2905 images with a resolution of 1024*1024 pixels. A series of preprocessing operations has been applied to this dataset. The performance results obtained in this study confirm that the classification obtained by the Xception network is the most precise for detecting cases infected with COVID19. Our system has achieved accuracy and sensitivity of 98% and 100% respectively.