Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification

Zuherman Rustam; Sri Hartini; Titin Siswantining; Dea Aulia Utami; Nadisa Karina Putri

doi:10.1007/978-3-030-36664-3_11

Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification

Zuherman Rustam, Sri Hartini, Titin Siswantining, Dea Aulia Utami, Nadisa Karina Putri

Department of Mathematics

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

5 Citations (Scopus)

Abstract

Soft Tissue Tumor (STT) are cell growths, whose existence are not limited to the presence of tumors in soft tissues. Furthermore, they are classified into soft tissue and non-soft tissue tumor and early detection is important to determine the right course of treatment. This research, therefore, aims to compare fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines on Soft Tissue Tumor dataset, obtained from Nur Hidayah Hospital, Yogyakarta, Indonesia, consisting of 50 STT and 25 non-STT samples. The results conclude that fuzzy kernel c-means provides a better running time when using the parameter σ = 0.05. However, support vector machines, with the parameter σ = 0.0001 performs better than other methods in terms of accuracy, sensitivity, precision, and F1-Score.

Original language	English
Title of host publication	Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Volume 2 - Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health
Editors	Mostafa Ezziyyani
Publisher	Springer
Pages	92-105
Number of pages	14
ISBN (Print)	9783030366636
DOIs	https://doi.org/10.1007/978-3-030-36664-3_11
Publication status	Published - 1 Jan 2020
Event	2nd International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Marrakech, Morocco Duration: 8 Jul 2019 → 11 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1103 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	2nd International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019
Country/Territory	Morocco
City	Marrakech
Period	8/07/19 → 11/07/19

Keywords

Classification
Fuzzy kernel C-Means
Fuzzy possibilistic C-Means
Kernel function
Soft tissue tumor
Support vector machines

Access to Document

10.1007/978-3-030-36664-3_11

Cite this

Rustam, Z., Hartini, S., Siswantining, T., Utami, D. A., & Putri, N. K. (2020). Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification. In M. Ezziyyani (Ed.), Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Volume 2 - Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health (pp. 92-105). (Advances in Intelligent Systems and Computing; Vol. 1103 AISC). Springer. https://doi.org/10.1007/978-3-030-36664-3_11

Rustam, Zuherman ; Hartini, Sri ; Siswantining, Titin et al. / Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification. Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Volume 2 - Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health. editor / Mostafa Ezziyyani. Springer, 2020. pp. 92-105 (Advances in Intelligent Systems and Computing).

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title = "Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification",

abstract = "Soft Tissue Tumor (STT) are cell growths, whose existence are not limited to the presence of tumors in soft tissues. Furthermore, they are classified into soft tissue and non-soft tissue tumor and early detection is important to determine the right course of treatment. This research, therefore, aims to compare fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines on Soft Tissue Tumor dataset, obtained from Nur Hidayah Hospital, Yogyakarta, Indonesia, consisting of 50 STT and 25 non-STT samples. The results conclude that fuzzy kernel c-means provides a better running time when using the parameter σ = 0.05. However, support vector machines, with the parameter σ = 0.0001 performs better than other methods in terms of accuracy, sensitivity, precision, and F1-Score.",

keywords = "Classification, Fuzzy kernel C-Means, Fuzzy possibilistic C-Means, Kernel function, Soft tissue tumor, Support vector machines",

author = "Zuherman Rustam and Sri Hartini and Titin Siswantining and Utami, {Dea Aulia} and Putri, {Nadisa Karina}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 2nd International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 ; Conference date: 08-07-2019 Through 11-07-2019",

year = "2020",

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series = "Advances in Intelligent Systems and Computing",

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Rustam, Z, Hartini, S, Siswantining, T, Utami, DA & Putri, NK 2020, Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification. in M Ezziyyani (ed.), Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Volume 2 - Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health. Advances in Intelligent Systems and Computing, vol. 1103 AISC, Springer, pp. 92-105, 2nd International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019, Marrakech, Morocco, 8/07/19. https://doi.org/10.1007/978-3-030-36664-3_11

Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification. / Rustam, Zuherman; Hartini, Sri; Siswantining, Titin et al.
Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Volume 2 - Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health. ed. / Mostafa Ezziyyani. Springer, 2020. p. 92-105 (Advances in Intelligent Systems and Computing; Vol. 1103 AISC).

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

TY - GEN

T1 - Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification

AU - Rustam, Zuherman

AU - Hartini, Sri

AU - Siswantining, Titin

AU - Utami, Dea Aulia

AU - Putri, Nadisa Karina

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Soft Tissue Tumor (STT) are cell growths, whose existence are not limited to the presence of tumors in soft tissues. Furthermore, they are classified into soft tissue and non-soft tissue tumor and early detection is important to determine the right course of treatment. This research, therefore, aims to compare fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines on Soft Tissue Tumor dataset, obtained from Nur Hidayah Hospital, Yogyakarta, Indonesia, consisting of 50 STT and 25 non-STT samples. The results conclude that fuzzy kernel c-means provides a better running time when using the parameter σ = 0.05. However, support vector machines, with the parameter σ = 0.0001 performs better than other methods in terms of accuracy, sensitivity, precision, and F1-Score.

AB - Soft Tissue Tumor (STT) are cell growths, whose existence are not limited to the presence of tumors in soft tissues. Furthermore, they are classified into soft tissue and non-soft tissue tumor and early detection is important to determine the right course of treatment. This research, therefore, aims to compare fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines on Soft Tissue Tumor dataset, obtained from Nur Hidayah Hospital, Yogyakarta, Indonesia, consisting of 50 STT and 25 non-STT samples. The results conclude that fuzzy kernel c-means provides a better running time when using the parameter σ = 0.05. However, support vector machines, with the parameter σ = 0.0001 performs better than other methods in terms of accuracy, sensitivity, precision, and F1-Score.

KW - Classification

KW - Fuzzy kernel C-Means

KW - Fuzzy possibilistic C-Means

KW - Kernel function

KW - Soft tissue tumor

KW - Support vector machines

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DO - 10.1007/978-3-030-36664-3_11

M3 - Conference contribution

AN - SCOPUS:85080912761

SN - 9783030366636

T3 - Advances in Intelligent Systems and Computing

SP - 92

EP - 105

BT - Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Volume 2 - Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health

A2 - Ezziyyani, Mostafa

PB - Springer

T2 - 2nd International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

Y2 - 8 July 2019 through 11 July 2019

ER -

Rustam Z, Hartini S, Siswantining T, Utami DA, Putri NK. Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification. In Ezziyyani M, editor, Advanced Intelligent Systems for Sustainable Development, AI2SD 2019 - Volume 2 - Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health. Springer. 2020. p. 92-105. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-36664-3_11

Comparison between fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines in soft tissue tumor classification

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Keywords

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