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IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

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Impact factor: 8.15

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Paper Title: Covid19 X-rays Image Classification Using Machine Learning and Deep Learning
Authors Name: Nikhil Fande , Roshan Fande , Rupesh Wadibhasme , Bhavesh Wadibhasme
Unique Id: IJSDR2006108
Published In: Volume 5 Issue 6, June-2020
Abstract: The novel Coronavirus (Covid19) has firstly started in China and widely spread in various countries and approximately 4.1 Million cases have been found worldwide. There are a limited number of COVID-19 testing kits available in hospitals due to gradually increasing in cases on a daily basis. Therefore, in order to increase the testing, it is necessary to implement an auto detection system which will prevent the spread of Covid-19. In this work, we build the binary classifiers based on the machine learning and deep learning models on real image data in predicting positive case probability and provide comparison study of each model. The top features of images have been used for further modeling processes to test stability of binary classifiers by comparing their performance on separate data. We observe that support vector machines (SVM) and Logistic Regression is more stable than convolutional neural networks.
Keywords: COVID-19, Coronavirus, Machine Learning, Convolutional Neural Network, Chest X-ray, Support Vector Machine, Logistic Regression
Cite Article: "Covid19 X-rays Image Classification Using Machine Learning and Deep Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 6, page no.651 - 656, June-2020, Available :http://www.ijsdr.org/papers/IJSDR2006108.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR2006108
Registration ID:191945
Published In: Volume 5 Issue 6, June-2020
DOI (Digital Object Identifier):
Page No: 651 - 656
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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