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
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
收藏
页码:921 / 929
页数:9
相关论文
共 50 条
  • [41] Lung Disease Classification using Deep Convolutional Neural Network
    Tariq, Zeenat
    Shah, Sayed Khushal
    Lee, Yugyung
    2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2019, : 732 - 735
  • [42] Automatic Cataract Detection And Grading Using Deep Convolutional Neural Network
    Zhang, Linglin
    Li, Jianqiang
    Zhang, Li
    Han, He
    Liu, Bo
    Yang, Jijiang
    Wang, Qing
    PROCEEDINGS OF THE 2017 IEEE 14TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC 2017), 2017, : 60 - 65
  • [43] Automatic mandibular canal detection using a deep convolutional neural network
    Gloria Hyunjung Kwak
    Eun-Jung Kwak
    Jae Min Song
    Hae Ryoun Park
    Yun-Hoa Jung
    Bong-Hae Cho
    Pan Hui
    Jae Joon Hwang
    Scientific Reports, 10
  • [44] Automatic fabric defect detection using a deep convolutional neural network
    Jing, Jun-Feng
    Ma, Hao
    Zhang, Huan-Huan
    COLORATION TECHNOLOGY, 2019, 135 (03) : 213 - 223
  • [45] Automatic mandibular canal detection using a deep convolutional neural network
    Kwak, Gloria Hyunjung
    Kwak, Eun-Jung
    Song, Jae Min
    Park, Hae Ryoun
    Jung, Yun-Hoa
    Cho, Bong-Hae
    Hui, Pan
    Hwang, Jae Joon
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [46] Automatic COVID-19 Diagnosis System Based on Deep Convolutional Neural Networks
    Krishna, Sajja Tulasi
    Kalluri, Hemantha Kumar
    TRAITEMENT DU SIGNAL, 2022, 39 (04) : 1203 - 1211
  • [47] Hybrid deep neural network for automatic detection of COVID-19 using chest x-ray images
    Acharya, Upendra Kumar
    Ali, Mohammad Taha
    Ahmed, Mohd Kaif
    Siddiqui, Mohd Tabish
    Gupta, Harsh
    Kumar, Sandeep
    Mishra, Ajey Shakti
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2023, 33 (04) : 1129 - 1143
  • [48] Automated detection of COVID-19 from CT scan using convolutional neural network
    Mishra, Narendra Kumar
    Singh, Pushpendra
    Joshi, Shiv Dutt
    BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2021, 41 (02) : 572 - 588
  • [49] Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease
    Oliver, A. Sheryl
    Suresh, P.
    Mohanarathinam, A.
    Kadry, Seifedine
    Thinnukool, Orawit
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 70 (01): : 2031 - 2047
  • [50] Optimal deep dense convolutional neural network based classification model for COVID-19 disease
    Oliver, A. Sheryl
    Suresh, P.
    Mohanarathinam, A.
    Kadry, Seifedine
    Thinnukool, Orawit
    Thinnukool, Orawit (orawit.t@cmu.ac.th), 1600, Tech Science Press (70): : 2031 - 2047