COVID-19 detection with X-ray images by using transfer learning

被引:4
|
作者
Mahanty, Chandrakanta [1 ]
Kumar, Raghvendra [1 ]
Mishra, Brojo Kishore [1 ]
Barna, Cornel [2 ]
机构
[1] GIET Univ, Dept Comp Sci & Engn, Gunupur, Odisha, India
[2] Aurel Vlaicu Univ Arad, Fac Exact Sci, Arad, Romania
关键词
COVID-19; pneumonia; transfer learning; coronavirus; SVM; VGG16; Xception;
D O I
10.3233/JIFS-219273
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Coronavirus is an infectious disease induced by extreme acute respiratory syndrome coronavirus 2. Novel coronaviruses can lead to mild to serious symptoms, like tiredness, nausea, fever, dry cough and breathlessness. Coronavirus symptoms are close to influenza, pneumonia and common cold. So Coronavirus can only be confirmed with a diagnostic test. 218 countries and territories worldwide have reported a total of 59.6 million active cases of the COVID-19 and 1.4 million deaths as of November 24, 2020. Rapid, accurate and early medical diagnosis of the disease is vital at this stage. Researchers analyzed the CT and X-ray findings from a large number of patients with coronavirus pneumonia to draw their conclusions. In this paper, we applied Support Vector Machine (SVM) classifier. After that we moved on to deep transfer learning models such as VGG16 and Xception which are implemented using Keras and Tensor flow to detect positive coronavirus patient using X-ray images. VGG16 and Xception show better performances as compared to SVM. In our work, Xception gained an accuracy of 97.46% with 98% f-score.
引用
收藏
页码:1717 / 1726
页数:10
相关论文
共 50 条
  • [41] FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images
    Agrawal, Tarun
    Choudhary, Prakash
    [J]. EVOLVING SYSTEMS, 2022, 13 (04) : 519 - 533
  • [42] FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images
    Tarun Agrawal
    Prakash Choudhary
    [J]. Evolving Systems, 2022, 13 : 519 - 533
  • [43] COVID-19 detection in chest X-ray images using deep boosted hybrid learning
    Khan, Saddam Hussain
    Sohail, Anabia
    Khan, Asifullah
    Hassan, Mehdi
    Lee, Yeon Soo
    Alam, Jamshed
    Basit, Abdul
    Zubair, Saima
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 137
  • [44] Deep Learning Approach for COVID-19 Detection Based on X-Ray Images
    Alasasfeh, Hayat O.
    Alomari, Taqwa
    Ibbini, M. S.
    [J]. 2021 18TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD), 2021, : 1 - 6
  • [45] Detection of COVID-19 Cases Based on Deep Learning with X-ray Images
    Wang, Zhiqiang
    Zhang, Ke
    Wang, Bingyan
    [J]. ELECTRONICS, 2022, 11 (21)
  • [46] Large-scaled detection of COVID-19 from X-ray using transfer learning
    Ibrahim, Abdullahi Umar
    Kibarer, Ayse Gunnay
    Al-Turjman, Fadi
    Kaba, Serife
    [J]. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2023, 33 (04) : 1116 - 1128
  • [47] Identification of COVID-19 with Chest X-ray Images using Deep Learning
    Khandar, Punam
    Thaokar, Chetana
    [J]. INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING, 2021, 12 (05): : 694 - 700
  • [48] DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images
    Vandana Bhattacharjee
    Ankita Priya
    Nandini Kumari
    Shamama Anwar
    [J]. Wireless Personal Communications, 2023, 130 : 1399 - 1416
  • [49] COVID-19 detection in X-ray images using convolutional neural networks
    Arias-Garzon, Daniel
    Alzate-Grisales, Jesus Alejandro
    Orozco-Arias, Simon
    Arteaga-Arteaga, Harold Brayan
    Bravo-Ortiz, Mario Alejandro
    Mora-Rubio, Alejandro
    Saborit-Torres, Jose Manuel
    Serrano, Joaquim aengel Montell
    Vaya, Maria de la Iglesia
    Cardona-Morales, Oscar
    Tabares-Soto, Reinel
    [J]. MACHINE LEARNING WITH APPLICATIONS, 2021, 6
  • [50] DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images
    Bhattacharjee, Vandana
    Priya, Ankita
    Kumari, Nandini
    Anwar, Shamama
    [J]. WIRELESS PERSONAL COMMUNICATIONS, 2023, 130 (02) : 1399 - 1416