Open AccessIdentification of COVID-19 with Chest X-ray images using Deep learning
Author(s) -
Punam Khandar,
Chetana B. Thaokar
Publication year - 2021
Publication title -
international journal of next-generation computing
Language(s) - English
Resource type - Journals
eISSN - 2229-4678
pISSN - 0976-5034
DOI - 10.47164/ijngc.v12i5.465
Subject(s) - covid-19 , deep learning , pneumonia , identification (biology) , outbreak , artificial intelligence , medicine , computer science , radiology , disease , pathology , infectious disease (medical specialty) , botany , biology
Covid-19 had become an outbreak at the end of December 2019, it has become a nightmare for all. It resulted ina huge loss in the health, life and economic sector of a country. It is a common spreading disease. Its symptomsare similar to pneumonia, which make it very hard to distinguish. After a clinical study of COVID-19 infectedpatients, it is discovered that infected patients tend to have a lung infection after getting in contact with the virus.Chest X-ray and CT scans are the most widely used techniques for detecting lung related problems. As manycountries are economically deprived after this situation, Chest X-ray is opted over CT scan, as the X-ray is lessexpensive, fast and simple than CT scans. In the health sector, deep learning has always been a very effectivetechnique. Numerous sources of medical images help deep learning to improvise itself and help this techniqueto combat COVID-19 outbreak. In this paper, we have described the dataset and model formulation. Then weprovided the comparison and analysis of models those we have used for the experimentation purpose. It describesthe implementation of each model and their comparison on the basis of loss and accuracy. Finally, we havementioned the results and discussion along with the future scopes that we hope to cover later on.