UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 6
June-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2106437


Registration ID:
310969

Page Number

d245-d250

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Title

Deep Learning approach to detect COVID-19 from X-ray Images

Abstract

COVID-19 is a highly contagious viral infection that has a major impact on worldwide health. It also had a tremendous influence on the world economy. If positive cases are discovered early, the pandemic disease's spread can be hindered. Fever, cough, dyspnea, breathing issues, and viral pneumonia are among the flu-like symptoms experienced by COVID-19 patients. These signs, however, are negligible by themselves. Many individuals are asymptomatic yet have a positive COVID-19 chest CT scan and pathogenic test. As a result, in addition to symptoms, positive pathogenic tests and positive chest CT/X-Rays are used to diagnose the condition. In medical picture categorization, Deep Learning (DL) techniques, notably Convolutional Neural Networks (CNN), have been found to be successful. This study presents CNN and ResNet50 models for COVID-19 prediction from chest X-ray images. The findings achieved in COVID-19 prediction using CNN and ResNet50 with training and testing accuracy of 99.5 percent and 94 percent, respectively, highlight the applicability of Deep Learning models in illness prediction.

Key Words

X-ray images of chest, Prediction, COVID-19, CNN, ResNet50

Cite This Article

"Deep Learning approach to detect COVID-19 from X-ray Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.d245-d250, June-2021, Available :http://www.jetir.org/papers/JETIR2106437.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Deep Learning approach to detect COVID-19 from X-ray Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppd245-d250, June-2021, Available at : http://www.jetir.org/papers/JETIR2106437.pdf

Publication Details

Published Paper ID: JETIR2106437
Registration ID: 310969
Published In: Volume 8 | Issue 6 | Year June-2021
DOI (Digital Object Identifier):
Page No: d245-d250
Country: Bengaluru, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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