Version 1
: Received: 18 March 2020 / Approved: 19 March 2020 / Online: 19 March 2020 (13:49:49 CET)
Version 2
: Received: 19 April 2020 / Approved: 22 April 2020 / Online: 22 April 2020 (05:58:22 CEST)
How to cite:
Sethy, P.K.; Behera, S.K.; Ratha, P.K.; Biswas, P. Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine. Preprints2020, 2020030300
Sethy, P.K.; Behera, S.K.; Ratha, P.K.; Biswas, P. Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine. Preprints 2020, 2020030300
Sethy, P.K.; Behera, S.K.; Ratha, P.K.; Biswas, P. Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine. Preprints2020, 2020030300
APA Style
Sethy, P.K., Behera, S.K., Ratha, P.K., & Biswas, P. (2020). Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine. Preprints. https://doi.org/
Chicago/Turabian Style
Sethy, P.K., Pradyumna Kumar Ratha and Preesat Biswas. 2020 "Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine" Preprints. https://doi.org/
Abstract
The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.
Keywords
coronavirus; COVID-19; diagnosis; deep features; SVM
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
22 April 2020
Commenter:
Prabira Kumar Sethy
Commenter's Conflict of Interests:
Author
Comment:
The dataset is increased. Previously we only consider xray images of normal people and covid-19 patient.
But, in revised manuscript we have taken xray images of covid-19 patient, pneumonia patient and healthy people. And 124 images of each type. The maximum accuracy achieved is 98.66%.
Commenter: Prabira Kumar Sethy
Commenter's Conflict of Interests: Author
But, in revised manuscript we have taken xray images of covid-19 patient, pneumonia patient and healthy people. And 124 images of each type. The maximum accuracy achieved is 98.66%.