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The comparative study of deep learning neural network approaches for breast cancer diagnosis

Breast cancer is one of the life-threatening cancer that leads to the most death due to cancer among women. Early diagnosis might help to reduce mortality. Thus, this research aims to study different approaches to the deep learning neural network model for breast cancer early detection for better...

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Main Authors: Mohd Nasir, Haslinah, Brahin, Noor Mohd Ariff, Zainuddin, Suraya, Mispan, Mohd Syafiq, Md Isa, Ida Syafiza, Sha’abani, Mohd Nurul Al Hafiz
Format: Article
Language:English
Published: International Association Of Online Engineering 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27492/2/0260402082023281.PDF
http://eprints.utem.edu.my/id/eprint/27492/
https://online-journals.org/index.php/i-joe/article/view/34905
https://doi.org/10.3991/ijoe.v19i06.34905
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spelling my.utem.eprints.274922024-07-04T12:16:07Z http://eprints.utem.edu.my/id/eprint/27492/ The comparative study of deep learning neural network approaches for breast cancer diagnosis Mohd Nasir, Haslinah Brahin, Noor Mohd Ariff Zainuddin, Suraya Mispan, Mohd Syafiq Md Isa, Ida Syafiza Sha’abani, Mohd Nurul Al Hafiz Breast cancer is one of the life-threatening cancer that leads to the most death due to cancer among women. Early diagnosis might help to reduce mortality. Thus, this research aims to study different approaches to the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) is evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared to others in detecting breast cancer. ANN can deliver better results with the provided dataset. However, more improvement is needed for better performance to ensure that the approach used is reliable enough for early breast cancer diagnosis. International Association Of Online Engineering 2023 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27492/2/0260402082023281.PDF Mohd Nasir, Haslinah and Brahin, Noor Mohd Ariff and Zainuddin, Suraya and Mispan, Mohd Syafiq and Md Isa, Ida Syafiza and Sha’abani, Mohd Nurul Al Hafiz (2023) The comparative study of deep learning neural network approaches for breast cancer diagnosis. International Journal Of Online And Biomedical Engineering, 19 (6). pp. 127-140. ISSN 2626-8493 https://online-journals.org/index.php/i-joe/article/view/34905 https://doi.org/10.3991/ijoe.v19i06.34905
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Breast cancer is one of the life-threatening cancer that leads to the most death due to cancer among women. Early diagnosis might help to reduce mortality. Thus, this research aims to study different approaches to the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) is evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared to others in detecting breast cancer. ANN can deliver better results with the provided dataset. However, more improvement is needed for better performance to ensure that the approach used is reliable enough for early breast cancer diagnosis.
format Article
author Mohd Nasir, Haslinah
Brahin, Noor Mohd Ariff
Zainuddin, Suraya
Mispan, Mohd Syafiq
Md Isa, Ida Syafiza
Sha’abani, Mohd Nurul Al Hafiz
spellingShingle Mohd Nasir, Haslinah
Brahin, Noor Mohd Ariff
Zainuddin, Suraya
Mispan, Mohd Syafiq
Md Isa, Ida Syafiza
Sha’abani, Mohd Nurul Al Hafiz
The comparative study of deep learning neural network approaches for breast cancer diagnosis
author_facet Mohd Nasir, Haslinah
Brahin, Noor Mohd Ariff
Zainuddin, Suraya
Mispan, Mohd Syafiq
Md Isa, Ida Syafiza
Sha’abani, Mohd Nurul Al Hafiz
author_sort Mohd Nasir, Haslinah
title The comparative study of deep learning neural network approaches for breast cancer diagnosis
title_short The comparative study of deep learning neural network approaches for breast cancer diagnosis
title_full The comparative study of deep learning neural network approaches for breast cancer diagnosis
title_fullStr The comparative study of deep learning neural network approaches for breast cancer diagnosis
title_full_unstemmed The comparative study of deep learning neural network approaches for breast cancer diagnosis
title_sort comparative study of deep learning neural network approaches for breast cancer diagnosis
publisher International Association Of Online Engineering
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27492/2/0260402082023281.PDF
http://eprints.utem.edu.my/id/eprint/27492/
https://online-journals.org/index.php/i-joe/article/view/34905
https://doi.org/10.3991/ijoe.v19i06.34905
_version_ 1804070323362988032
score 13.211869

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