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Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, pages 1-16

Deep learning approach for disease detection in lumbosacral spine radiographs using ConvNet

Pablo De Abreu Vieira ^{1, 2}

Luis Vogado ^{1, 2}

Lucas Lopes ^{1, 2}

Ricardo Ricardo ^{1, 2}

Pedro Santos-Neto ^{1, 2}

Mano Joseph Mathew ³

Deborah Magalhães ^{1, 4}

Romuere Silva ^{1, 4}

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Graduate Program in Computer Science – UFMA/UFPI Association, Federal University of Piauí – (UFPI), Teresina, PI, Brazil |

Research Department, Maida Health Research and Development Department – (R&D), Teresina, PI, Brazil |

Information Systems, EFREI Research Lab - France, Picos, PI, Brazil |

⁴

Information Systems is the department, Information Systems Federal University of Piauí – (UFPI), Picos, PI, Brazil |

Publication type: Journal Article

Publication date: 2023-08-13

Taylor & Francis

Journal: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

scimago Q2

wos Q4

SJR: 0.486

CiteScore: 2.8

Impact factor: 1.3

ISSN: 21681163, 21681171

DOI: 10.1080/21681163.2023.2245922

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Computer Science Applications

Biomedical Engineering

Computational Mechanics

Radiology, Nuclear Medicine and imaging

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	1
Applied Sciences (Switzerland)	Applied Sciences (Switzerland), 1, 33.33% Applied Sciences (Switzerland) 1 publication, 33.33%
Biomedical Signal Processing and Control	Biomedical Signal Processing and Control, 1, 33.33% Biomedical Signal Processing and Control 1 publication, 33.33%
	1

	1
Institute of Electrical and Electronics Engineers (IEEE)	Institute of Electrical and Electronics Engineers (IEEE), 1, 33.33% Institute of Electrical and Electronics Engineers (IEEE) 1 publication, 33.33%
MDPI	MDPI, 1, 33.33% MDPI 1 publication, 33.33%
Elsevier	Elsevier, 1, 33.33% Elsevier 1 publication, 33.33%
	1

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Publisher

Taylor & Francis

Journal

Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

scimago Q2

wos Q4

SJR

0.486

CiteScore

2.8

Impact factor

1.3

ISSN

21681163 (Print)

21681171 (Electronic)

Deep learning approach for disease detection in lumbosacral spine radiographs using ConvNet

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