Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/58083
Title: Accurate prognosis of Covid-19 using CT scan images with deep learning model and machine learning classifiers
Authors: Gupta, Siddharth
Aggarwal, Palak
Chaubey, Nisha
Panwar, Avnish
Keywords: Machine Learning;Deep Learning;Coronavirus;Logistic regression (LR);Convolution neural network (CNN)
Issue Date: Mar-2021
Publisher: NIScPR-CSIR, India
Abstract: 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%.
Page(s): 19-24
URI: http://nopr.niscair.res.in/handle/123456789/58083
ISSN: 0975-105X (Online); 0367-8393 (Print)
Appears in Collections:IJRSP Vol.50(1) [March 2021]

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