A New Approach to Covid-19 Detection Using AlexNet from Chest CT Images

Abstract

At the beginning of 2020, the pneumonia-like covid-19 virus spread rapidly worldwide. With the emergence of this dangerous virus, work and daily life have become very difficult. In order to control this virus, all the centers are closed and quarantined by the government and the countries of the world. Every day, many people from all over the world die due to covid-19. Many efforts are being made in all fields to diagnose and treat people infected with this virus. For this reason, many researchers started working on identifying this virus and its types. The Scientifics, in computer science, did not sit idle either. In some studies, different image processing methods and algorithms have been used to extract the edge features of the CT images of lungs. Edge detection is the purpose of this article to reach the highest accuracy rate. Consequently, a classification method with CNN -AlexNet architecture was used. Therefore, the proposed method helps physicians for improving disease diagnosis from CT images of the lungs. It can help to more accurate detection of the infected lungs. The results show that the average accuracy rate of image edge extraction with a threshold value of 0.1 is 93% and the accuracy rate of AlexNet architecture classification is 100%.