An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image

Andhryn Celica Dewi Rahajeng; Mohammad Nuh; Nada Fitrieyatul Hikmah

doi:10.1109/CENIM51130.2020.9297941

An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image

Andhryn Celica Dewi Rahajeng
, Mohammad Nuh
, Nada Fitrieyatul Hikmah

Institut Teknologi Sepuluh Nopember

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

4 Citations (Scopus)

Abstract

Skin cancer has recently become one of the types of cancer that often appears and could become deadly. Mortality from skin cancer patient could be reduced if the detection and treatment is early and appropriate. Segmentation of skin lesions is usually on images that have classified melanocytic, whereas skin lesions that are classified as nonmelanocytic are equally important. Support vector machine (SVM) are used to differentiate skin lesions in dermoscopic images. The results of the classification, achieving best performance with accuracy of 85%, sensitivity of 86%, specification of 84%, and precision of 88% using radial basis function kernel. RBF kernel is giving best performance for this type of data. For validation model, this study using k-Fold Cross Validation. The optimal value are k=7 and k=8 with an accuracy of 83%. This study gives an idea to deal with disease which related to skin cancer using image processing technique.

Original language	English
Title of host publication	CENIM 2020 - Proceeding
Subtitle of host publication	International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	76-81
Number of pages	6
ISBN (Electronic)	9781728182834
DOIs	https://doi.org/10.1109/CENIM51130.2020.9297941
Publication status	Published - 17 Nov 2020
Event	2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 - Virtual, Surabaya, Indonesia Duration: 17 Nov 2020 → 18 Nov 2020

Publication series

Name	CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

Conference

Conference	2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Country/Territory	Indonesia
City	Virtual, Surabaya
Period	17/11/20 → 18/11/20

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

classification
imageprocessing
skin lesions

Access to Document

10.1109/CENIM51130.2020.9297941

Cite this

Rahajeng, A. C. D., Nuh, M., & Hikmah, N. F. (2020). An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image. In CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (pp. 76-81). Article 9297941 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CENIM51130.2020.9297941

Rahajeng, Andhryn Celica Dewi ; Nuh, Mohammad ; Hikmah, Nada Fitrieyatul. / An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image. CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 76-81 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020).

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title = "An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image",

abstract = "Skin cancer has recently become one of the types of cancer that often appears and could become deadly. Mortality from skin cancer patient could be reduced if the detection and treatment is early and appropriate. Segmentation of skin lesions is usually on images that have classified melanocytic, whereas skin lesions that are classified as nonmelanocytic are equally important. Support vector machine (SVM) are used to differentiate skin lesions in dermoscopic images. The results of the classification, achieving best performance with accuracy of 85\%, sensitivity of 86\%, specification of 84\%, and precision of 88\% using radial basis function kernel. RBF kernel is giving best performance for this type of data. For validation model, this study using k-Fold Cross Validation. The optimal value are k=7 and k=8 with an accuracy of 83\%. This study gives an idea to deal with disease which related to skin cancer using image processing technique.",

keywords = "classification, imageprocessing, skin lesions",

author = "Rahajeng, \{Andhryn Celica Dewi\} and Mohammad Nuh and Hikmah, \{Nada Fitrieyatul\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 ; Conference date: 17-11-2020 Through 18-11-2020",

year = "2020",

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doi = "10.1109/CENIM51130.2020.9297941",

language = "English",

series = "CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020",

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Rahajeng, ACD, Nuh, M & Hikmah, NF 2020, An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image. in CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020., 9297941, CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020, Institute of Electrical and Electronics Engineers Inc., pp. 76-81, 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020, Virtual, Surabaya, Indonesia, 17/11/20. https://doi.org/10.1109/CENIM51130.2020.9297941

An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image. / Rahajeng, Andhryn Celica Dewi; Nuh, Mohammad ; Hikmah, Nada Fitrieyatul.
CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 76-81 9297941 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020).

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

TY - GEN

T1 - An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image

AU - Rahajeng, Andhryn Celica Dewi

AU - Nuh, Mohammad

AU - Hikmah, Nada Fitrieyatul

PY - 2020/11/17

Y1 - 2020/11/17

N2 - Skin cancer has recently become one of the types of cancer that often appears and could become deadly. Mortality from skin cancer patient could be reduced if the detection and treatment is early and appropriate. Segmentation of skin lesions is usually on images that have classified melanocytic, whereas skin lesions that are classified as nonmelanocytic are equally important. Support vector machine (SVM) are used to differentiate skin lesions in dermoscopic images. The results of the classification, achieving best performance with accuracy of 85%, sensitivity of 86%, specification of 84%, and precision of 88% using radial basis function kernel. RBF kernel is giving best performance for this type of data. For validation model, this study using k-Fold Cross Validation. The optimal value are k=7 and k=8 with an accuracy of 83%. This study gives an idea to deal with disease which related to skin cancer using image processing technique.

AB - Skin cancer has recently become one of the types of cancer that often appears and could become deadly. Mortality from skin cancer patient could be reduced if the detection and treatment is early and appropriate. Segmentation of skin lesions is usually on images that have classified melanocytic, whereas skin lesions that are classified as nonmelanocytic are equally important. Support vector machine (SVM) are used to differentiate skin lesions in dermoscopic images. The results of the classification, achieving best performance with accuracy of 85%, sensitivity of 86%, specification of 84%, and precision of 88% using radial basis function kernel. RBF kernel is giving best performance for this type of data. For validation model, this study using k-Fold Cross Validation. The optimal value are k=7 and k=8 with an accuracy of 83%. This study gives an idea to deal with disease which related to skin cancer using image processing technique.

KW - classification

KW - imageprocessing

KW - skin lesions

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DO - 10.1109/CENIM51130.2020.9297941

M3 - Conference contribution

AN - SCOPUS:85099665590

T3 - CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

SP - 76

EP - 81

BT - CENIM 2020 - Proceeding

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020

Y2 - 17 November 2020 through 18 November 2020

ER -

Rahajeng ACD, Nuh M , Hikmah NF. An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image. In CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 76-81. 9297941. (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020). doi: 10.1109/CENIM51130.2020.9297941

An Evaluation Performance of Kernel on Support Vector Machine to Classify the Skin Tumors in Dermoscopy Image

Abstract

Publication series

Conference

UN SDGs

Keywords

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