Open AccessDetection of COVID-19 using Hybrid ResNet and SVM
Author(s) -
Vamsidhar Enireddy,
Mathe John Kenny Kumar,
Babitha Donepudi,
C. Karthikeyan
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/993/1/012046
Subject(s) - support vector machine , covid-19 , corona (planetary geology) , artificial intelligence , deep learning , virus , virus diseases , computer science , pattern recognition (psychology) , classifier (uml) , viral pneumonia , pneumonia , virology , biology , medicine , infectious disease (medical specialty) , pathology , disease , astrobiology , outbreak , venus
The whole world facing a huge crisis because of Corona virus also known as COVID-2019, identified first in December 2019 in the city of Wuhan located in China. The detection of persons infected with the virus is most important as it can be spread easily from him to others and also the person infected with the virus may not know that he is infected until a number of symptoms fallout from him. In this paper the virus detection is done using deep learning and machine learning algorithms using the X-ray images. A dataset is created with three classes consisting of normal, corona virus, and pneumonia images. The proposed method uses ResNet50 and SVM, deep learning features are extracted using ResNet50 and classification is done using SVM classifier. The classification accuracy obtained from the model is 100% when testing on the Corona virus and normal images, whereas the results obtained from the model is 94% when it is tested on the dataset consisting of normal, Corona virus and pneumonia images and performed well compared to VGG16.