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Accurate prognosis of Covid-19 using CT scan images with deep learning model and machine learning classifiers

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Title Accurate prognosis of Covid-19 using CT scan images with deep learning model and machine learning classifiers
 
Creator Gupta, Siddharth
Aggarwal, Palak
Chaubey, Nisha
Panwar, Avnish
 
Subject Machine Learning
Deep Learning
Coronavirus
Logistic regression (LR)
Convolution neural network (CNN)
 
Description 19-24
The Covid-19 disease is caused by coronavirus or SARS-CoV-2 has wrecked havoc globally. This epidemic severely impacted the economy of most of the countries across the world and has taken away many lives. To control the pandemic situation many researchers, organizations, and institutes have come up with the pathogenesis and developing vaccines to decimate this disease. Out of the several techniques, one of the techniques use image patterns on Computed Tomography (CT) to detect whether a patient is Covid-19 positive or not. In this work, the SARS-COV-2 dataset has been used for the detection of Covid-19 images and normal images. These dataset images have been fed to various deep learning models for extracting the features and finally passed to various ML classifiers which classify the images as Covid-19 or normal images. The results have established that the VGG19 model along with Logistic Regression (LR) classifier gives the maximum AUC and accuracy of 98.5% and 94.6%.
 
Date 2021-09-09T10:29:30Z
2021-09-09T10:29:30Z
2021-03
 
Type Article
 
Identifier 0975-105X (Online); 0367-8393 (Print)
http://nopr.niscair.res.in/handle/123456789/58083
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJRSP Vol.50(1) [March 2021]