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Deep learning method based for breast cancer classification

The most prevalent cancer in women worldwide and one of the main factors in cancer-related mortality is breast cancer. Extensive research efforts have been dedicated to early detection, diagnosis, and treatment of breast cancer to reduce mortality rates. This research aims to achieve accurate diagno...

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Main Authors: Irmawati, Irmawati, Ernawan, Ferda, Fakhreldin, Mohammad Adam Ibrahim, Saryoko, Andi
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/40298/1/Deep%20learning%20method%20based%20for%20breast%20cancer%20classification.pdf
http://umpir.ump.edu.my/id/eprint/40298/2/Deep%20learning%20method%20based%20for%20breast%20cancer%20classification_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40298/
https://doi.org/10.1109/ICITRI59340.2023.10249318
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spelling my.ump.umpir.402982024-04-16T04:03:15Z http://umpir.ump.edu.my/id/eprint/40298/ Deep learning method based for breast cancer classification Irmawati, Irmawati Ernawan, Ferda Fakhreldin, Mohammad Adam Ibrahim Saryoko, Andi QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) The most prevalent cancer in women worldwide and one of the main factors in cancer-related mortality is breast cancer. Extensive research efforts have been dedicated to early detection, diagnosis, and treatment of breast cancer to reduce mortality rates. This research aims to achieve accurate diagnosis of breast cancer and classify breast cancer using deep learning method. The study proposes deep learning techniques with Adam's optimization and two hidden layers for breast cancer classification. Addressing challenges such as data instability and overfitting during deep learning training, the research focuses on updating network weights. The experiments examine two hidden layers and varying learning rates to enhance classification accuracy. The datasets utilized in the experiments include the WBCD dataset (Original), the WDBC dataset (Diagnostics), and the Coimbra dataset. Additionally, the proposed scheme's accuracy is compared against existing benchmarks for breast cancer detection. The experimental findings show that the suggested scheme outperforms other benchmarks, achieving an impressive 96% accuracy in breast cancer classification. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40298/1/Deep%20learning%20method%20based%20for%20breast%20cancer%20classification.pdf pdf en http://umpir.ump.edu.my/id/eprint/40298/2/Deep%20learning%20method%20based%20for%20breast%20cancer%20classification_ABS.pdf Irmawati, Irmawati and Ernawan, Ferda and Fakhreldin, Mohammad Adam Ibrahim and Saryoko, Andi (2023) Deep learning method based for breast cancer classification. In: 2nd International Conference on Information Technology Research and Innovation, ICITRI 2023 , 16 August 2023 , Virtual, Online. pp. 13-16. (192770). ISBN 979-835032494-5 https://doi.org/10.1109/ICITRI59340.2023.10249318
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Irmawati, Irmawati
Ernawan, Ferda
Fakhreldin, Mohammad Adam Ibrahim
Saryoko, Andi
Deep learning method based for breast cancer classification
description The most prevalent cancer in women worldwide and one of the main factors in cancer-related mortality is breast cancer. Extensive research efforts have been dedicated to early detection, diagnosis, and treatment of breast cancer to reduce mortality rates. This research aims to achieve accurate diagnosis of breast cancer and classify breast cancer using deep learning method. The study proposes deep learning techniques with Adam's optimization and two hidden layers for breast cancer classification. Addressing challenges such as data instability and overfitting during deep learning training, the research focuses on updating network weights. The experiments examine two hidden layers and varying learning rates to enhance classification accuracy. The datasets utilized in the experiments include the WBCD dataset (Original), the WDBC dataset (Diagnostics), and the Coimbra dataset. Additionally, the proposed scheme's accuracy is compared against existing benchmarks for breast cancer detection. The experimental findings show that the suggested scheme outperforms other benchmarks, achieving an impressive 96% accuracy in breast cancer classification.
format Conference or Workshop Item
author Irmawati, Irmawati
Ernawan, Ferda
Fakhreldin, Mohammad Adam Ibrahim
Saryoko, Andi
author_facet Irmawati, Irmawati
Ernawan, Ferda
Fakhreldin, Mohammad Adam Ibrahim
Saryoko, Andi
author_sort Irmawati, Irmawati
title Deep learning method based for breast cancer classification
title_short Deep learning method based for breast cancer classification
title_full Deep learning method based for breast cancer classification
title_fullStr Deep learning method based for breast cancer classification
title_full_unstemmed Deep learning method based for breast cancer classification
title_sort deep learning method based for breast cancer classification
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40298/1/Deep%20learning%20method%20based%20for%20breast%20cancer%20classification.pdf
http://umpir.ump.edu.my/id/eprint/40298/2/Deep%20learning%20method%20based%20for%20breast%20cancer%20classification_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40298/
https://doi.org/10.1109/ICITRI59340.2023.10249318
_version_ 1822924219535589376
score 13.232414

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