Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection

Mukta Sharma; Lokesh Sharma; Ming Jer Jeng; Liann Be Chang; Shiang Fu Huang; Shih Lin Wu

doi:10.1109/IUCC/DSCI/SmartCNS.2019.00134

Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection

Mukta Sharma
, Lokesh Sharma
, Ming Jer Jeng
, Liann Be Chang
, Shiang Fu Huang
, Shih Lin Wu^*

^*Corresponding author for this work

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

4 Scopus citations

Abstract

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	644-647
Number of pages	4
ISBN (Electronic)	9781728152097
DOIs	https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00134
State	Published - 10 2019
Event	2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019 - Shenyang, China Duration: 21 10 2019 → 23 10 2019

Publication series

Name	Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

Conference

Conference	2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019
Country/Territory	China
City	Shenyang
Period	21/10/19 → 23/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

AdaBoost
Bagging
Boosting
LDA
Meta-learning
QDA
Raman spectroscopy

UN SDGs

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

Access to Document

10.1109/IUCC/DSCI/SmartCNS.2019.00134

Cite this

Sharma, M., Sharma, L., Jeng, M. J., Chang, L. B., Huang, S. F., & Wu, S. L. (2019). Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection. In Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019 (pp. 644-647). Article 8982720 (Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00134

Sharma, Mukta ; Sharma, Lokesh ; Jeng, Ming Jer et al. / Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection. Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 644-647 (Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019).

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title = "Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection",

abstract = "Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.",

keywords = "AdaBoost, Bagging, Boosting, LDA, Meta-learning, QDA, Raman spectroscopy",

author = "Mukta Sharma and Lokesh Sharma and Jeng, \{Ming Jer\} and Chang, \{Liann Be\} and Huang, \{Shiang Fu\} and Wu, \{Shih Lin\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019 ; Conference date: 21-10-2019 Through 23-10-2019",

year = "2019",

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doi = "10.1109/IUCC/DSCI/SmartCNS.2019.00134",

language = "英语",

series = "Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019",

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Sharma, M, Sharma, L, Jeng, MJ, Chang, LB, Huang, SF & Wu, SL 2019, Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection. in Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019., 8982720, Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 644-647, 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019, Shenyang, China, 21/10/19. https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00134

Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection. / Sharma, Mukta; Sharma, Lokesh; Jeng, Ming Jer et al.
Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 644-647 8982720 (Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019).

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

TY - GEN

T1 - Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection

AU - Sharma, Mukta

AU - Sharma, Lokesh

AU - Jeng, Ming Jer

AU - Chang, Liann Be

AU - Huang, Shiang Fu

AU - Wu, Shih Lin

PY - 2019/10

Y1 - 2019/10

N2 - Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.

AB - Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.

KW - AdaBoost

KW - Bagging

KW - Boosting

KW - LDA

KW - Meta-learning

KW - QDA

KW - Raman spectroscopy

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U2 - 10.1109/IUCC/DSCI/SmartCNS.2019.00134

DO - 10.1109/IUCC/DSCI/SmartCNS.2019.00134

M3 - 会议稿件

AN - SCOPUS:85081096778

T3 - Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

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BT - Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

Y2 - 21 October 2019 through 23 October 2019

ER -

Sharma M, Sharma L, Jeng MJ, Chang LB, Huang SF , Wu SL. Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection. In Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 644-647. 8982720. (Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019). doi: 10.1109/IUCC/DSCI/SmartCNS.2019.00134

Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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Cite this