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Fusion: Practice and Applications
Volume 2 , Issue 1, PP: 05-13 , 2020 | Cite this article as | XML | Html |PDF


An efficient deep belief network for Detection of Coronavirus Disease COVID-19

Authors Names :   Shaymaa Adnan Abdulrahma   1 *     Abdel-Badeeh M. Salem   2  

1  Affiliation :  Department of Computer Engineering, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq and a PhD Student at Ain Shams University, Egypt

    Email :  Shaymaaa416@gmail.com

2  Affiliation :  Department of Computer &Information Science, Ain Shams University, Cairo, Egypt

    Email :  absalem@cis.asu.edu.eg

Doi   :   https://doi.org/10.54216/FPA.020102

Received: March 05, 2020 Revised: June 12, 2020 Accepted: July 01, 2020

Abstract :


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.com/muhammedtalo/COVID-19). The results show that the proposed system identifies the COVID-19 cases with an accuracy of 90%.

Keywords :

COVID-19; machine learning; deep learning; X-ray; Image processing

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Cite this Article as :
Shaymaa Adnan Abdulrahma , Abdel-Badeeh M. Salem, An efficient deep belief network for Detection of Coronavirus Disease COVID-19, Fusion: Practice and Applications, Vol. 2 , No. 1 , (2020) : 05-13 (Doi   :  https://doi.org/10.54216/FPA.020102)