BEMD-3DCNN-Based Method for COVID-19 Detection

Abstract

Coronavirus outbreak continues to spread around the world and none knows when it will stop. Therefore, from the first day of the virus detection in Wuhan, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the right medicine to help and protect patients. A fast diagnostic and detection system is a priority and should be found to stop COVID-19 from expanding. The purpose of the study is to combine the bi-dimensional empirical mode decomposition (BEMD) technique with 3DCNN to detect COVID-19. BEMD is used to decompose the original images into IMFs and from there built a video then apply the 3DCNN to classify and detect COVID-19 virus. In our experiment we used 6484 X-Ray images, 1802 of them were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed techniques achieved the desired results on the selected dataset. It reached the accuracy of 100%.