Fusion: Practice and Applications
FPA
2692-4048
2770-0070
10.54216/FPA
https://www.americaspg.com/journals/show/543
2018
2018
An efficient deep belief network for Detection of Coronavirus Disease COVID-19
Department of Computer Engineering, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq and a PhD Student at Ain Shams University, Egypt
Shaymaa
Shaymaa
Department of Computer &Information Science, Ain Shams University, Cairo, Egypt
Abdel-Badeeh M.
Salem
COVID-19 infection is one of the most dangerous respiratory viruses, and the early detection of this disease reduces the speed of its spread among people. The goal of this virus is to infect the lung by creating patchy white shadows inside the lungs. This paper presents an intelligent method based on the deep learning technique to analyze the medical images of respiratory diseases. Two data set was used in this experiment first dataset is normal lungs taken from the Kaggle data repository. In contrast, abnormal lungs were taken from (https:github.commuhammedtaloCOVID-19). The results show that the proposed system identifies the COVID-19 cases with an accuracy of 90%.
2020
2020
05
13
10.54216/FPA.020102
https://www.americaspg.com/articleinfo/3/show/543