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

Department of Medical Informatics and Artificial Intelligence (RIB/RIB1)

Silesian University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018
Editors	Ewa Pietka, Pawel Badura, Jacek Kawa, Wojciech Wieclawek
Publisher	Springer Verlag
Pages	279-289
Number of pages	11
ISBN (Print)	9783319912103
DOIs	https://doi.org/10.1007/978-3-319-91211-0_25
Publication status	Published - 2019
Event	6th International Conference on Information Technology in Biomedicine, ITIB 2018 - Kamien Slaski, Poland Duration: 18 Jun 2018 → 20 Jun 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	762
ISSN (Print)	2194-5357

Conference

Conference	6th International Conference on Information Technology in Biomedicine, ITIB 2018
Country/Territory	Poland
City	Kamien Slaski
Period	18/06/18 → 20/06/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Classification
Computer-aided diagnosis
Lung cancer
Random forest
Support vector machine

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

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

Cite this

Choroba, B., & Badura, P. (2019). Attribute-based assessment of lung nodules in CT using support vector machine and random forest. In E. Pietka, P. Badura, J. Kawa, & W. Wieclawek (Eds.), Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018 (pp. 279-289). (Advances in Intelligent Systems and Computing; Vol. 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. editor / Ewa Pietka ; Pawel Badura ; Jacek Kawa ; Wojciech Wieclawek. Springer Verlag, 2019. pp. 279-289 (Advances in Intelligent Systems and Computing).

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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",

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language = "English",

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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",

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Choroba, B & Badura, P 2019, Attribute-based assessment of lung nodules in CT using support vector machine and random forest. in E Pietka, P Badura, J Kawa & W Wieclawek (eds), Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018. Advances in Intelligent Systems and Computing, vol. 762, Springer Verlag, pp. 279-289, 6th International Conference on Information Technology in Biomedicine, ITIB 2018, Kamien Slaski, Poland, 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. ed. / Ewa Pietka; Pawel Badura; Jacek Kawa; Wojciech Wieclawek. Springer Verlag, 2019. p. 279-289 (Advances in Intelligent Systems and Computing; Vol. 762).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Attribute-based assessment of lung nodules in CT using support vector machine and random forest

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

ER -

Choroba B, Badura P. Attribute-based assessment of lung nodules in CT using support vector machine and random forest. In Pietka E, Badura P, Kawa J, Wieclawek W, editors, Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB’2018. Springer Verlag. 2019. p. 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

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this