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Deep Transfer Learning Approach for Detection of Covid-19 from Chest X-Ray Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 40 |
| Year of Publication: 2020 |
| Authors: Lakshay Goyal, Nitin Arora |
10.5120/ijca2020920505
|
Lakshay Goyal, Nitin Arora . Deep Transfer Learning Approach for Detection of Covid-19 from Chest X-Ray Images. International Journal of Computer Applications. 176, 40 ( Jul 2020), 21-25. DOI=10.5120/ijca2020920505
Abstract
COVID-19 is an irresistible illness brought about by the most as of late found coronavirus. This new infection and malady were obscure before the episode started in Wuhan, China, in December 2019 [1]. COVID-19 is currently a pandemic influencing numerous nations universally.Analyzing after which diagnosing is presently a significant assignment. The Coronavirus (COVID-19) pandemic has led to the most significant number of employees globally bound to work remotely. With the advancements in computer algorithms and particularly Artificial Intelligence, the detection of this type of virus in the early stages will help in the speedy recovery and assist in freeing the pressure off healthcare operations. This paper focuses on the arrangement which can help in the examination of COVID-19 with a regular chest X-rays utilizing deep learning techniques.The primary approach is to collect all the possible images for COVID-19 that exist and use the Convolutional Neural Network to generate more images to help in the detection of the virus from the usable X-rays images with the highest accuracy possible. The number of images in the collected dataset is 748 images for three different types of classes. The classes are the COVID-19, normal, pneumonia bacterial. Three deep transfer models are selected in this research for investigation. The models are the VGG19, VGG16, and Restnet50.
References
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