Automatic detection of pneumonia analyzing ultrasound digital images

Ronald Barrientos; Avid Roman-Gonzalez; Franklin Barrientos; Leonardo Solis; Malena Correa; Monica Pajuelo; Cynthia Anticona; Roberto Lavarello; Benjamin Castaneda; Richard Oberhelman; William Checkley; Robert H. Gilman; Mirko Zimic

doi:10.1109/CONCAPAN.2016.7942375

Automatic detection of pneumonia analyzing ultrasound digital images

Ronald Barrientos, Avid Roman-Gonzalez, Franklin Barrientos, Leonardo Solis, Malena Correa, Monica Pajuelo, Cynthia Anticona, Roberto Lavarello, Benjamin Castaneda, Richard Oberhelman, William Checkley, Robert H. Gilman, Mirko Zimic

Sección de Electricidad y Electrónica

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

17 Citas (Scopus)

Resumen

Pneumonia is one of the major causes of child mortality. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. This paper presents a method for automatic diagnostics of pneumonia using ultrasound imaging of the lungs. The approach presented here is based on the analysis of patterns present in rectangular segments from the ultrasound digital images. Specific features from the characteristic vectors were obtained and classified with standard neural networks. A training and testing set of positive and negative vectors were compiled. Vectors obtained from a single patient were included only in the testing or in the training set, but never in both. Our approach was able to correctly classify vectors with evidence of pneumonia, with 91.5% sensitivity and 100% specificity.

Idioma original	Inglés
Título de la publicación alojada	2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781467395786
DOI	https://doi.org/10.1109/CONCAPAN.2016.7942375
Estado	Publicada - 2 jul. 2016
Evento	36th IEEE Central American and Panama Convention, CONCAPAN 2016 - San Jose, Costa Rica Duración: 9 nov. 2016 → 11 nov. 2016

Serie de la publicación

Nombre	2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016

Conferencia

Conferencia	36th IEEE Central American and Panama Convention, CONCAPAN 2016
País/Territorio	Costa Rica
Ciudad	San Jose
Período	9/11/16 → 11/11/16

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/CONCAPAN.2016.7942375

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Barrientos, R., Roman-Gonzalez, A., Barrientos, F., Solis, L., Correa, M., Pajuelo, M., Anticona, C., Lavarello, R., Castaneda, B., Oberhelman, R., Checkley, W., Gilman, R. H., & Zimic, M. (2016). Automatic detection of pneumonia analyzing ultrasound digital images. En 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016 Artículo 7942375 (2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONCAPAN.2016.7942375

@inproceedings{ca382321bda04b73a50872d899759e34,

title = "Automatic detection of pneumonia analyzing ultrasound digital images",

abstract = "Pneumonia is one of the major causes of child mortality. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. This paper presents a method for automatic diagnostics of pneumonia using ultrasound imaging of the lungs. The approach presented here is based on the analysis of patterns present in rectangular segments from the ultrasound digital images. Specific features from the characteristic vectors were obtained and classified with standard neural networks. A training and testing set of positive and negative vectors were compiled. Vectors obtained from a single patient were included only in the testing or in the training set, but never in both. Our approach was able to correctly classify vectors with evidence of pneumonia, with 91.5\% sensitivity and 100\% specificity.",

keywords = "Pneumonia, echography, image processing, remote diagnostics, ultrasound",

author = "Ronald Barrientos and Avid Roman-Gonzalez and Franklin Barrientos and Leonardo Solis and Malena Correa and Monica Pajuelo and Cynthia Anticona and Roberto Lavarello and Benjamin Castaneda and Richard Oberhelman and William Checkley and Gilman, \{Robert H.\} and Mirko Zimic",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE Central American and Panama Convention, CONCAPAN 2016 ; Conference date: 09-11-2016 Through 11-11-2016",

year = "2016",

month = jul,

day = "2",

doi = "10.1109/CONCAPAN.2016.7942375",

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booktitle = "2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016",

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Barrientos, R, Roman-Gonzalez, A, Barrientos, F, Solis, L, Correa, M, Pajuelo, M, Anticona, C, Lavarello, R , Castaneda, B, Oberhelman, R, Checkley, W, Gilman, RH & Zimic, M 2016, Automatic detection of pneumonia analyzing ultrasound digital images. En 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016., 7942375, 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016, Institute of Electrical and Electronics Engineers Inc., 36th IEEE Central American and Panama Convention, CONCAPAN 2016, San Jose, Costa Rica, 9/11/16. https://doi.org/10.1109/CONCAPAN.2016.7942375

Automatic detection of pneumonia analyzing ultrasound digital images. / Barrientos, Ronald; Roman-Gonzalez, Avid; Barrientos, Franklin et al.
2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7942375 (2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Automatic detection of pneumonia analyzing ultrasound digital images

AU - Barrientos, Ronald

AU - Roman-Gonzalez, Avid

AU - Barrientos, Franklin

AU - Solis, Leonardo

AU - Correa, Malena

AU - Pajuelo, Monica

AU - Anticona, Cynthia

AU - Lavarello, Roberto

AU - Castaneda, Benjamin

AU - Oberhelman, Richard

AU - Checkley, William

AU - Gilman, Robert H.

AU - Zimic, Mirko

PY - 2016/7/2

Y1 - 2016/7/2

N2 - Pneumonia is one of the major causes of child mortality. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. This paper presents a method for automatic diagnostics of pneumonia using ultrasound imaging of the lungs. The approach presented here is based on the analysis of patterns present in rectangular segments from the ultrasound digital images. Specific features from the characteristic vectors were obtained and classified with standard neural networks. A training and testing set of positive and negative vectors were compiled. Vectors obtained from a single patient were included only in the testing or in the training set, but never in both. Our approach was able to correctly classify vectors with evidence of pneumonia, with 91.5% sensitivity and 100% specificity.

AB - Pneumonia is one of the major causes of child mortality. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. This paper presents a method for automatic diagnostics of pneumonia using ultrasound imaging of the lungs. The approach presented here is based on the analysis of patterns present in rectangular segments from the ultrasound digital images. Specific features from the characteristic vectors were obtained and classified with standard neural networks. A training and testing set of positive and negative vectors were compiled. Vectors obtained from a single patient were included only in the testing or in the training set, but never in both. Our approach was able to correctly classify vectors with evidence of pneumonia, with 91.5% sensitivity and 100% specificity.

KW - Pneumonia

KW - echography

KW - image processing

KW - remote diagnostics

KW - ultrasound

UR - https://www.scopus.com/pages/publications/85021415999

U2 - 10.1109/CONCAPAN.2016.7942375

DO - 10.1109/CONCAPAN.2016.7942375

M3 - Conference contribution

AN - SCOPUS:85021415999

T3 - 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016

BT - 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 36th IEEE Central American and Panama Convention, CONCAPAN 2016

Y2 - 9 November 2016 through 11 November 2016

ER -

Barrientos R, Roman-Gonzalez A, Barrientos F, Solis L, Correa M, Pajuelo M et al. Automatic detection of pneumonia analyzing ultrasound digital images. En 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7942375. (2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016). doi: 10.1109/CONCAPAN.2016.7942375

Automatic detection of pneumonia analyzing ultrasound digital images

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