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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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40298/ 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 |
| _version_ | 1848826010404913152 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:38:01Z |
| format | Conference or Workshop Item |
| id | ump-40298 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:38:01Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) https://doi.org/10.1109/ICITRI59340.2023.10249318 |
| 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 |
| title | 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_short | Deep learning method based for breast cancer classification |
| title_sort | deep learning method based for breast cancer classification |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/40298/ http://umpir.ump.edu.my/id/eprint/40298/ 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 |