Deep learning method based for breast cancer classification

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

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Abstract

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.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Adam optimization; Breast cancer; Classification method; Deep learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	16 Apr 2024 04:03
Last Modified:	16 Apr 2024 04:03
URI:	http://umpir.ump.edu.my/id/eprint/40298
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