eFis: A fuzzy inference method for predicting malignancy of small pulmonary nodules

Aydın Kaya; Ahmet Burak Can

doi:10.1007/978-3-319-11755-3_29

eFis: A fuzzy inference method for predicting malignancy of small pulmonary nodules

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

1 Citation (Scopus)

Abstract

Predicting malignancy of small pulmonary nodules from computer tomography scans is a difficult and important problem to diagnose lung cancer. This paper presents a rule based fuzzy inference method for predicting malignancy rating of small pulmonary nodules. We use the nodule characteristics provided by Lung Image Database Consortium dataset to determine malignancy rating. The proposed fuzzy inference method uses outputs of ensemble classifiers and rules from radiologist agreements on the nodules. The results are evaluated over classification accuracy performance and compared with single classifier methods. We observed that the preliminary results are very promising and system is open to development.

Original language	English
Title of host publication	Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings
Editors	Aurélio Campilho, Aurélio Campilho, Mohamed S. Kamel
Publisher	Springer Verlag
Pages	255-262
Number of pages	8
ISBN (Electronic)	9783319117546
DOIs	https://doi.org/10.1007/978-3-319-11755-3_29
Publication status	Published - 2014
Event	11th International Conference on Image Analysis and Recognition, ICIAR 2014 - Vilamoura, Portugal Duration: 22 Oct 2014 → 24 Oct 2014

Publication series

Name	Lecture Notes in Computer Science
Volume	8815
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Image Analysis and Recognition, ICIAR 2014
Country/Territory	Portugal
City	Vilamoura
Period	22/10/14 → 24/10/14

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Ensemble classifiers
Fuzzy inference
Nodule characteristics
Small pulmonary nodules

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10.1007/978-3-319-11755-3_29

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Kaya, A., & Can, A. B. (2014). eFis: A fuzzy inference method for predicting malignancy of small pulmonary nodules. In A. Campilho, A. Campilho, & M. S. Kamel (Eds.), Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings (pp. 255-262). (Lecture Notes in Computer Science; Vol. 8815). Springer Verlag. https://doi.org/10.1007/978-3-319-11755-3_29

@inproceedings{9f9738fc7da8483fb546091862a5b340,

title = "eFis: A fuzzy inference method for predicting malignancy of small pulmonary nodules",

abstract = "Predicting malignancy of small pulmonary nodules from computer tomography scans is a difficult and important problem to diagnose lung cancer. This paper presents a rule based fuzzy inference method for predicting malignancy rating of small pulmonary nodules. We use the nodule characteristics provided by Lung Image Database Consortium dataset to determine malignancy rating. The proposed fuzzy inference method uses outputs of ensemble classifiers and rules from radiologist agreements on the nodules. The results are evaluated over classification accuracy performance and compared with single classifier methods. We observed that the preliminary results are very promising and system is open to development.",

keywords = "Ensemble classifiers, Fuzzy inference, Nodule characteristics, Small pulmonary nodules",

author = "Aydın Kaya and Can, \{Ahmet Burak\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 11th International Conference on Image Analysis and Recognition, ICIAR 2014 ; Conference date: 22-10-2014 Through 24-10-2014",

year = "2014",

doi = "10.1007/978-3-319-11755-3\_29",

language = "English",

series = "Lecture Notes in Computer Science",

publisher = "Springer Verlag",

pages = "255--262",

editor = "Aur{\'e}lio Campilho and Aur{\'e}lio Campilho and Kamel, \{Mohamed S.\}",

booktitle = "Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings",

address = "Germany",

}

Kaya, A & Can, AB 2014, eFis: A fuzzy inference method for predicting malignancy of small pulmonary nodules. in A Campilho, A Campilho & MS Kamel (eds), Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings. Lecture Notes in Computer Science, vol. 8815, Springer Verlag, pp. 255-262, 11th International Conference on Image Analysis and Recognition, ICIAR 2014, Vilamoura, Portugal, 22/10/14. https://doi.org/10.1007/978-3-319-11755-3_29

eFis: A fuzzy inference method for predicting malignancy of small pulmonary nodules. / Kaya, Aydın ; Can, Ahmet Burak.
Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings. ed. / Aurélio Campilho; Aurélio Campilho; Mohamed S. Kamel. Springer Verlag, 2014. p. 255-262 (Lecture Notes in Computer Science; Vol. 8815).

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

TY - GEN

T1 - eFis

T2 - 11th International Conference on Image Analysis and Recognition, ICIAR 2014

AU - Kaya, Aydın

AU - Can, Ahmet Burak

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - Predicting malignancy of small pulmonary nodules from computer tomography scans is a difficult and important problem to diagnose lung cancer. This paper presents a rule based fuzzy inference method for predicting malignancy rating of small pulmonary nodules. We use the nodule characteristics provided by Lung Image Database Consortium dataset to determine malignancy rating. The proposed fuzzy inference method uses outputs of ensemble classifiers and rules from radiologist agreements on the nodules. The results are evaluated over classification accuracy performance and compared with single classifier methods. We observed that the preliminary results are very promising and system is open to development.

AB - Predicting malignancy of small pulmonary nodules from computer tomography scans is a difficult and important problem to diagnose lung cancer. This paper presents a rule based fuzzy inference method for predicting malignancy rating of small pulmonary nodules. We use the nodule characteristics provided by Lung Image Database Consortium dataset to determine malignancy rating. The proposed fuzzy inference method uses outputs of ensemble classifiers and rules from radiologist agreements on the nodules. The results are evaluated over classification accuracy performance and compared with single classifier methods. We observed that the preliminary results are very promising and system is open to development.

KW - Ensemble classifiers

KW - Fuzzy inference

KW - Nodule characteristics

KW - Small pulmonary nodules

UR - https://www.scopus.com/pages/publications/84908676249

U2 - 10.1007/978-3-319-11755-3_29

DO - 10.1007/978-3-319-11755-3_29

M3 - Conference contribution

AN - SCOPUS:84908676249

T3 - Lecture Notes in Computer Science

SP - 255

EP - 262

BT - Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings

A2 - Campilho, Aurélio

A2 - Kamel, Mohamed S.

PB - Springer Verlag

Y2 - 22 October 2014 through 24 October 2014

ER -

eFis: A fuzzy inference method for predicting malignancy of small pulmonary nodules

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