Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

Indah Werdiningsih; Badrus Zaman; Barry Nuqoba

doi:10.1063/1.4994407

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

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

2 Citations (Scopus)

Abstract

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

Original language	English
Title of host publication	International Conference on Mathematics - Pure, Applied and Computation
Subtitle of host publication	Empowering Engineering using Mathematics
Editors	Dieky Adzkiya
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735415478
DOIs	https://doi.org/10.1063/1.4994407
Publication status	Published - 1 Aug 2017
Event	2nd International Conference on Mathematics - Pure, Applied and Computation: Empowering Engineering using Mathematics, ICoMPAC 2016 - Surabaya, Indonesia Duration: 23 Nov 2016 → …

Publication series

Name	AIP Conference Proceedings
Volume	1867
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	2nd International Conference on Mathematics - Pure, Applied and Computation: Empowering Engineering using Mathematics, ICoMPAC 2016
Country/Territory	Indonesia
City	Surabaya
Period	23/11/16 → …

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1063/1.4994407

Cite this

Werdiningsih, I., Zaman, B., & Nuqoba, B. (2017). Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer. In D. Adzkiya (Ed.), International Conference on Mathematics - Pure, Applied and Computation: Empowering Engineering using Mathematics Article 020004 (AIP Conference Proceedings; Vol. 1867). American Institute of Physics Inc.. https://doi.org/10.1063/1.4994407

Werdiningsih, Indah ; Zaman, Badrus ; Nuqoba, Barry. / Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer. International Conference on Mathematics - Pure, Applied and Computation: Empowering Engineering using Mathematics. editor / Dieky Adzkiya. American Institute of Physics Inc., 2017. (AIP Conference Proceedings).

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title = "Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer",

abstract = "This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 \%. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.",

author = "Indah Werdiningsih and Badrus Zaman and Barry Nuqoba",

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Werdiningsih, I , Zaman, B & Nuqoba, B 2017, Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer. in D Adzkiya (ed.), International Conference on Mathematics - Pure, Applied and Computation: Empowering Engineering using Mathematics., 020004, AIP Conference Proceedings, vol. 1867, American Institute of Physics Inc., 2nd International Conference on Mathematics - Pure, Applied and Computation: Empowering Engineering using Mathematics, ICoMPAC 2016, Surabaya, Indonesia, 23/11/16. https://doi.org/10.1063/1.4994407

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer. / Werdiningsih, Indah ; Zaman, Badrus ; Nuqoba, Barry.
International Conference on Mathematics - Pure, Applied and Computation: Empowering Engineering using Mathematics. ed. / Dieky Adzkiya. American Institute of Physics Inc., 2017. 020004 (AIP Conference Proceedings; Vol. 1867).

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

TY - GEN

T1 - Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

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AU - Nuqoba, Barry

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N2 - This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

AB - This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

UR - https://www.scopus.com/pages/publications/85027961359

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DO - 10.1063/1.4994407

M3 - Conference contribution

AN - SCOPUS:85027961359

T3 - AIP Conference Proceedings

BT - International Conference on Mathematics - Pure, Applied and Computation

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PB - American Institute of Physics Inc.

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Werdiningsih I , Zaman B , Nuqoba B. Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer. In Adzkiya D, editor, International Conference on Mathematics - Pure, Applied and Computation: Empowering Engineering using Mathematics. American Institute of Physics Inc. 2017. 020004. (AIP Conference Proceedings). doi: 10.1063/1.4994407

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

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