Attribute-based assessment of lung nodules in CT using support vector machine and random forest

Beata Choroba; Pawel Badura

doi:10.1007/978-3-319-91211-0_25

Attribute-based assessment of lung nodules in CT using support vector machine and random forest

Beata Choroba
, Pawel Badura

Katedra Informatyki Medycznej i Sztucznej Inteligencji (RIB/RIB1)

Silesian University of Technology

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

1 Cytowanie z bazy Scopus

Abstrakt

An attempt to provide a tool for detection of certain attributes of pulmonary nodules is presented in this paper. The support vector machine and random forest are employed to determine the nodule calcification and likelihood of malignancy on the basis of ten intensity and geometric features. Training and validation relies on lung nodule cases from the public LIDC-IDRI database with over a thousand computed tomography studies. Lesion annotation provided by four radiologists in terms of delineation and quantitative assessment of selected attributes yields ca. 2500 nodules available for the analysis. In both classifications involving two classifiers the accuracy exceeding 80% was achieved.

Język oryginału	angielski
Tytuł publikacji goszczącej	Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018
Redaktorzy	Ewa Pietka, Pawel Badura, Jacek Kawa, Wojciech Wieclawek
Wydawca	Springer Verlag
Strony	279-289
Liczba stron	11
ISBN (drukowany)	9783319912103
Identyfikatory DOI	https://doi.org/10.1007/978-3-319-91211-0_25
Status publikacji	Opublikowano - 2019
Wydarzenie	6th International Conference on Information Technology in Biomedicine, ITIB 2018 - Kamien Slaski, Polska Czas trwania: 18 cze 2018 → 20 cze 2018

Seria publikacji

Nazwa	Advances in Intelligent Systems and Computing
Tom	762
ISSN (drukowany)	2194-5357

Konferencja

Konferencja	6th International Conference on Information Technology in Biomedicine, ITIB 2018
Kraj/Terytorium	Polska
Miejscowość	Kamien Slaski
Okres	18/06/18 → 20/06/18

Cele SDG ONZ

Ten wynik przyczynia się do realizacji następujących celów zrównoważonego rozwoju

Cel 3 Dobre zdrowie i dobre samopoczucie

Obszary tematyczne ASJC Scopus

Inżynieria sterowania i systemów
Informatyka ogólna

Uzyskaj dostęp do dokumentu

10.1007/978-3-319-91211-0_25

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Link do publikacji w Scopus

Cytowanie

Choroba, B., & Badura, P. (2019). Attribute-based assessment of lung nodules in CT using support vector machine and random forest. W E. Pietka, P. Badura, J. Kawa, & W. Wieclawek (Red.), Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018 (str. 279-289). (Advances in Intelligent Systems and Computing; Tom 762). Springer Verlag. https://doi.org/10.1007/978-3-319-91211-0_25

Choroba, Beata ; Badura, Pawel. / Attribute-based assessment of lung nodules in CT using support vector machine and random forest. Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018. redaktor / Ewa Pietka ; Pawel Badura ; Jacek Kawa ; Wojciech Wieclawek. Springer Verlag, 2019. str. 279-289 (Advances in Intelligent Systems and Computing).

@inproceedings{07d0fb3418a24eccb6dbd62ac415bcae,

title = "Attribute-based assessment of lung nodules in CT using support vector machine and random forest",

abstract = "An attempt to provide a tool for detection of certain attributes of pulmonary nodules is presented in this paper. The support vector machine and random forest are employed to determine the nodule calcification and likelihood of malignancy on the basis of ten intensity and geometric features. Training and validation relies on lung nodule cases from the public LIDC-IDRI database with over a thousand computed tomography studies. Lesion annotation provided by four radiologists in terms of delineation and quantitative assessment of selected attributes yields ca. 2500 nodules available for the analysis. In both classifications involving two classifiers the accuracy exceeding 80\% was achieved.",

keywords = "Classification, Computer-aided diagnosis, Lung cancer, Random forest, Support vector machine",

author = "Beata Choroba and Pawel Badura",

note = "Publisher Copyright: {\textcopyright} 2019, Springer International Publishing AG, part of Springer Nature.; 6th International Conference on Information Technology in Biomedicine, ITIB 2018 ; Conference date: 18-06-2018 Through 20-06-2018",

year = "2019",

doi = "10.1007/978-3-319-91211-0\_25",

language = "English",

isbn = "9783319912103",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "279--289",

editor = "Ewa Pietka and Pawel Badura and Jacek Kawa and Wojciech Wieclawek",

booktitle = "Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB{\textquoteright}2018",

address = "Germany",

}

Choroba, B & Badura, P 2019, Attribute-based assessment of lung nodules in CT using support vector machine and random forest. w E Pietka, P Badura, J Kawa & W Wieclawek (red), Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018. Advances in Intelligent Systems and Computing, tom 762, Springer Verlag, str. 279-289, 6th International Conference on Information Technology in Biomedicine, ITIB 2018, Kamien Slaski, Polska, 18/06/18. https://doi.org/10.1007/978-3-319-91211-0_25

Attribute-based assessment of lung nodules in CT using support vector machine and random forest. / Choroba, Beata; Badura, Pawel.
Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018. red. / Ewa Pietka; Pawel Badura; Jacek Kawa; Wojciech Wieclawek. Springer Verlag, 2019. str. 279-289 (Advances in Intelligent Systems and Computing; Tom 762).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

TY - GEN

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AU - Choroba, Beata

AU - Badura, Pawel

PY - 2019

Y1 - 2019

N2 - An attempt to provide a tool for detection of certain attributes of pulmonary nodules is presented in this paper. The support vector machine and random forest are employed to determine the nodule calcification and likelihood of malignancy on the basis of ten intensity and geometric features. Training and validation relies on lung nodule cases from the public LIDC-IDRI database with over a thousand computed tomography studies. Lesion annotation provided by four radiologists in terms of delineation and quantitative assessment of selected attributes yields ca. 2500 nodules available for the analysis. In both classifications involving two classifiers the accuracy exceeding 80% was achieved.

AB - An attempt to provide a tool for detection of certain attributes of pulmonary nodules is presented in this paper. The support vector machine and random forest are employed to determine the nodule calcification and likelihood of malignancy on the basis of ten intensity and geometric features. Training and validation relies on lung nodule cases from the public LIDC-IDRI database with over a thousand computed tomography studies. Lesion annotation provided by four radiologists in terms of delineation and quantitative assessment of selected attributes yields ca. 2500 nodules available for the analysis. In both classifications involving two classifiers the accuracy exceeding 80% was achieved.

KW - Classification

KW - Computer-aided diagnosis

KW - Lung cancer

KW - Random forest

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/85048466733

U2 - 10.1007/978-3-319-91211-0_25

DO - 10.1007/978-3-319-91211-0_25

M3 - Conference contribution

AN - SCOPUS:85048466733

SN - 9783319912103

T3 - Advances in Intelligent Systems and Computing

SP - 279

EP - 289

BT - Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018

A2 - Pietka, Ewa

A2 - Badura, Pawel

A2 - Kawa, Jacek

A2 - Wieclawek, Wojciech

PB - Springer Verlag

T2 - 6th International Conference on Information Technology in Biomedicine, ITIB 2018

Y2 - 18 June 2018 through 20 June 2018

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Choroba B, Badura P. Attribute-based assessment of lung nodules in CT using support vector machine and random forest. W Pietka E, Badura P, Kawa J, Wieclawek W, redaktorzy, Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018. Springer Verlag. 2019. str. 279-289. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-91211-0_25

Attribute-based assessment of lung nodules in CT using support vector machine and random forest

Abstrakt

Seria publikacji

Konferencja

Cele SDG ONZ

Obszary tematyczne ASJC Scopus

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Inne pliki i linki

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