UGC Approved Journal no 63975(19)

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

Volume 9 Issue 7
July-2022
eISSN: 2349-5162

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

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Published Paper ID:
JETIR2207378


Registration ID:
405987

Page Number

d604-d614

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Title

DETECTION OF CLINICAL FEATURES OF COVID-19 PATIENTS BY DEEP LEARNING TRANSFER MODEL

Authors

Abstract

The first signs of the COVID-19 pandemic were discovered in December of this year. COVID-19 was blamed for 1.4 million fatalities by the year 2020. A worldwide pandemic was declared by WHO because of the large number of fatalities because of COVID-19 or SARS-CoV-2. Fever, dry cough, exhaustion, and a diminished sense of smell and taste have been recorded in persons who have been exposed to COVID-19, and many have been admitted to critical care units for urgent intermittent required breathing (IMV). In order to mitigate the damages caused by this epidemic, immediate actions were necessary. WHO recommends widespread usage of COVID-19 testing to combat spread of such disease. It’s imperative that an automated detection method for COVID-19 detection be developed and used as an alternate diagnostic option due to the restricted quantity of COVID-19 testing supplies accessible in medical institutions. When it comes to the accurate finding of illness, chest Xray is often the first imaging tool used. With the use of computer vision & deep learning, it is possible to identify COVID-19 viruses in chest X-ray pictures. Utilising CNN for photo classification & prediction is being successful due to abundance of large-scale digital image database. COVID19 might be identified from a chest X-ray using an intelligent clinical decision support system (SADC) that is more accessible. That's why we've amassed 566 radiological pictures, all of which have been divided in 3 types: pneumonia-type, & healthy-type. As into experimental assessment, 70% of data were utilized for training and 30% for testing. In addition to the pre-processing, the image is augmented by rotating, flipping horizontally, shifting channels, and rescaling. f1 score of 98.60 percent and a sensitivity of 98.30 percent were reached by the final classifier, making it the most accurate and sensitive. Recognizing COVID-19 in x-ray pictures using the suggested method thus proves its efficacy.

Key Words

COVID-19, CNN, VGG19 architecture, MobileNet architecture, InceptionV3 architecture

Cite This Article

"DETECTION OF CLINICAL FEATURES OF COVID-19 PATIENTS BY DEEP LEARNING TRANSFER MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.d604-d614, July-2022, Available :http://www.jetir.org/papers/JETIR2207378.pdf

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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

"DETECTION OF CLINICAL FEATURES OF COVID-19 PATIENTS BY DEEP LEARNING TRANSFER MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppd604-d614, July-2022, Available at : http://www.jetir.org/papers/JETIR2207378.pdf

Publication Details

Published Paper ID: JETIR2207378
Registration ID: 405987
Published In: Volume 9 | Issue 7 | Year July-2022
DOI (Digital Object Identifier):
Page No: d604-d614
Country: kalaburagi, karnataka, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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