Automatic detection lung infected COVID-19 disease using deep learning (Convolutional Neural Network)

被引：0

作者：

Alameady, Mali H. Hakem ^{[1
]}

Fahad, Ahmed ^{[2
]}

Abdullah, Alaa ^{[3
]}

机构：

[1] Univ Kufa, Fac Comp Sci & Maths, Dept Comp Sci, Najaf, Iraq

[2] Univ Thi Qar, Al Nassiriya 64001, Iraq

[3] Minist Educ, Educ Directorate Thi Qar, Thi Qar, Iraq

来源：

INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS | 2021年 / 12卷 / 02期

关键词：

Deep learning; Convolutional Neural Network; COVID-19;

D O I：

10.22075/ijnaa.2021.5148

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In late 2019, a virus appeared suddenly he claims Covid-19, which started in China and began to spread very widely around the world. And because of its effects, which are not limited to human life only, but rather in economic and social aspects, and because of the increase in daily injuries and significantly with the limited hospitals that cannot accommodate these large numbers, it is necessary to find an automatic and rapid detection method that limits the spread of the disease and its detection at an early stage in order to be treated more quickly. In this paper, deep learning was relied upon to create a CNN model to detect COVID-19 infected lungs using chest X-ray images. The base consists of a set of images taken of lungs infected with Covid-19 disease and normal lungs, as the CNN structure gave accuracy, Precision, Recall and F-Measure 100%.

引用

页码：921 / 929

页数：9

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