A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

Prabhjot Kaur, Shilpi Harnal, Rajeev Tiwari, Fahd S. Alharithi, Ahmed H. Almulihi, Irene Delgado Noya and Nitin Goyal
Additional contact information
Prabhjot Kaur: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Shilpi Harnal: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Rajeev Tiwari: Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India
Fahd S. Alharithi: Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
Ahmed H. Almulihi: Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
Irene Delgado Noya: Higher Polytechnic School/Industrial Organization Engineering, Universidad Europea del Atlántico, 39011 Santander, Spain
Nitin Goyal: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India

IJERPH, 2021, vol. 18, issue 22, 1-17

Abstract: COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

Keywords: convolutional neural network; COVID-19; disease detection; InceptionV4; SVM; chest XR images (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/22/12191/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/22/12191/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:22:p:12191-:d:683984

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().