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Application of deep learning method in facilitating the detection of breast cancer

Breast cancer is a type of tumour that could be treated if the disease is identified at an earlier stage. Early diagnosis is crucial when it comes to reducing the mortality rate. In this study, deep neural network method is applied to facilitate the detection of breast cancer. The aim of this study...

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Main Authors: Samah, Azurah A., Nasien, Dewi, Hashim, Haslina, Sahar, Julia, Abdul Majid, Hairudin, Yusoff, Yusliza, Ali Shah, Zuraini
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/92514/1/AzurahASamah2020_ApplicationofDeepLearningMethod.pdf
http://eprints.utm.my/id/eprint/92514/
http://dx.doi.org/10.1088/1757-899X/864/1/012079
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spelling my.utm.925142021-09-30T15:14:57Z http://eprints.utm.my/id/eprint/92514/ Application of deep learning method in facilitating the detection of breast cancer Samah, Azurah A. Nasien, Dewi Hashim, Haslina Sahar, Julia Abdul Majid, Hairudin Yusoff, Yusliza Ali Shah, Zuraini QA75 Electronic computers. Computer science Breast cancer is a type of tumour that could be treated if the disease is identified at an earlier stage. Early diagnosis is crucial when it comes to reducing the mortality rate. In this study, deep neural network method is applied to facilitate the detection of breast cancer. The aim of this study is to implement deep neural network in breast cancer classification models that can produce high classification accuracy. Deep Neural Network (DNN) with multiple hidden layers was applied to learn deep features of the breast cancer data. Dataset used in this study was obtained from the UCI Machine Learning Repository which consists of Wisconsin Breast Cancer Dataset (WBCD) and used for the original and diagnostic dataset. The performance of the proposed DNN method was compared against previous machine learning classifier in terms of accuracy. From the results, the accuracy obtained for the original dataset was 97.14% and 97.66% for the diagnostic dataset, which is better than previous SVM method. 2020-07-09 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/92514/1/AzurahASamah2020_ApplicationofDeepLearningMethod.pdf Samah, Azurah A. and Nasien, Dewi and Hashim, Haslina and Sahar, Julia and Abdul Majid, Hairudin and Yusoff, Yusliza and Ali Shah, Zuraini (2020) Application of deep learning method in facilitating the detection of breast cancer. In: 2nd Joint Conference on Green Engineering Technology and Applied Computing 2020, IConGETech 2020 and International Conference on Applied Computing 2020, ICAC 2020, 4 February 2020 - 5 February 2020, Bangkok, Thailand. http://dx.doi.org/10.1088/1757-899X/864/1/012079
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Samah, Azurah A.
Nasien, Dewi
Hashim, Haslina
Sahar, Julia
Abdul Majid, Hairudin
Yusoff, Yusliza
Ali Shah, Zuraini
Application of deep learning method in facilitating the detection of breast cancer
description Breast cancer is a type of tumour that could be treated if the disease is identified at an earlier stage. Early diagnosis is crucial when it comes to reducing the mortality rate. In this study, deep neural network method is applied to facilitate the detection of breast cancer. The aim of this study is to implement deep neural network in breast cancer classification models that can produce high classification accuracy. Deep Neural Network (DNN) with multiple hidden layers was applied to learn deep features of the breast cancer data. Dataset used in this study was obtained from the UCI Machine Learning Repository which consists of Wisconsin Breast Cancer Dataset (WBCD) and used for the original and diagnostic dataset. The performance of the proposed DNN method was compared against previous machine learning classifier in terms of accuracy. From the results, the accuracy obtained for the original dataset was 97.14% and 97.66% for the diagnostic dataset, which is better than previous SVM method.
format Conference or Workshop Item
author Samah, Azurah A.
Nasien, Dewi
Hashim, Haslina
Sahar, Julia
Abdul Majid, Hairudin
Yusoff, Yusliza
Ali Shah, Zuraini
author_facet Samah, Azurah A.
Nasien, Dewi
Hashim, Haslina
Sahar, Julia
Abdul Majid, Hairudin
Yusoff, Yusliza
Ali Shah, Zuraini
author_sort Samah, Azurah A.
title Application of deep learning method in facilitating the detection of breast cancer
title_short Application of deep learning method in facilitating the detection of breast cancer
title_full Application of deep learning method in facilitating the detection of breast cancer
title_fullStr Application of deep learning method in facilitating the detection of breast cancer
title_full_unstemmed Application of deep learning method in facilitating the detection of breast cancer
title_sort application of deep learning method in facilitating the detection of breast cancer
publishDate 2020
url http://eprints.utm.my/id/eprint/92514/1/AzurahASamah2020_ApplicationofDeepLearningMethod.pdf
http://eprints.utm.my/id/eprint/92514/
http://dx.doi.org/10.1088/1757-899X/864/1/012079
_version_ 1713199743002214400
score 13.211869

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