UGC Approved Journal no 63975(19)

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

Volume 5 Issue 6
June-2018
eISSN: 2349-5162

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

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Published Paper ID:
JETIRFR06009


Registration ID:
501073

Page Number

45-52

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Title

Convolutional Neural Network for Tuberculosis Diagnosis: A Review

Abstract

Tuberculosis is life-threatening infectious disease that mostly affects the lungs. Small droplets thrown into the air by coughs or sneezes spread tuberculosis germs from one person to the next. The approach for detecting active tuberculosis and latent infection has remained essentially constant throughout the previous few decades. The tuberculin skin test, which employs the purified protein derivative to identify latent infection, is still used today. Researchers developed a deep CNN based on the computer aided diagnosis method for the automated tuberculosis screening in this study. The author used the influence of transfer learning to acquire TB screening results of 0.96, 0.94, and 0.88 in terms of AUC for three real-world datasets based on large-scale chest X-rays. More precise and quick tuberculosis diagnosis is necessary than ever before in the present worldwide tuberculosis pandemic, which includes a significant number of patients infected with the human immunodeficiency virus and growing rates of multidrug-resistant tuberculosis. Deep learning has become the gold standard in the field of machine learning. Deep convolutional brain networks have been shown to be a viable solution for a variety of visual applications, most notably in PC vision. Deep CNN, which considers start to finish preparation from element extraction to gathering without the need for particular has highlight layout, may well be investigated further

Key Words

Chest X-Rays (CXR), Convolutional Neural Network (CNN), Diagnosis, Human Immunodeficiency Virus Infection (HIV), Tuberculosis (TB)

Cite This Article

"Convolutional Neural Network for Tuberculosis Diagnosis: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.45-52, June-2018, Available :http://www.jetir.org/papers/JETIRFR06009.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

"Convolutional Neural Network for Tuberculosis Diagnosis: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp45-52, June-2018, Available at : http://www.jetir.org/papers/JETIRFR06009.pdf

Publication Details

Published Paper ID: JETIRFR06009
Registration ID: 501073
Published In: Volume 5 | Issue 6 | Year June-2018
DOI (Digital Object Identifier):
Page No: 45-52
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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