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Journal Central

@article{ART003017270},
author={Gui Rae Jo and Beomsu Baek and Young-Soon Kim and Dong Hoon Lim},
title={Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={11},
pages={1-11},
doi={10.9708/jksci.2023.28.11.001}

TY - JOUR
AU - Gui Rae Jo
AU - Beomsu Baek
AU - Young-Soon Kim
AU - Dong Hoon Lim
TI - Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 11
PB - The Korean Society Of Computer And Information
SP - 1
EP - 11
SN - 1598-849X
AB - Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.
KW - Transfer learning;Deep learning;Breast cancer;DNN-SVM Hybrid model
DO - 10.9708/jksci.2023.28.11.001
ER -

Gui Rae Jo, Beomsu Baek, Young-Soon Kim and Dong Hoon Lim. (2023). Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification. Journal of The Korea Society of Computer and Information, 28(11), 1-11.

Gui Rae Jo, Beomsu Baek, Young-Soon Kim and Dong Hoon Lim. 2023, "Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification", Journal of The Korea Society of Computer and Information, vol.28, no.11 pp.1-11. Available from: doi:10.9708/jksci.2023.28.11.001

Gui Rae Jo, Beomsu Baek, Young-Soon Kim, Dong Hoon Lim "Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification" Journal of The Korea Society of Computer and Information 28.11 pp.1-11 (2023) : 1.

Gui Rae Jo, Beomsu Baek, Young-Soon Kim, Dong Hoon Lim. Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification. 2023; 28(11), 1-11. Available from: doi:10.9708/jksci.2023.28.11.001

Gui Rae Jo, Beomsu Baek, Young-Soon Kim and Dong Hoon Lim. "Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification" Journal of The Korea Society of Computer and Information 28, no.11 (2023) : 1-11.doi: 10.9708/jksci.2023.28.11.001

Gui Rae Jo; Beomsu Baek; Young-Soon Kim; Dong Hoon Lim. Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification. Journal of The Korea Society of Computer and Information, 28(11), 1-11. doi: 10.9708/jksci.2023.28.11.001

Gui Rae Jo; Beomsu Baek; Young-Soon Kim; Dong Hoon Lim. Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification. Journal of The Korea Society of Computer and Information. 2023; 28(11) 1-11. doi: 10.9708/jksci.2023.28.11.001

Gui Rae Jo, Beomsu Baek, Young-Soon Kim, Dong Hoon Lim. Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification. 2023; 28(11), 1-11. Available from: doi:10.9708/jksci.2023.28.11.001

Gui Rae Jo, Beomsu Baek, Young-Soon Kim and Dong Hoon Lim. "Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification" Journal of The Korea Society of Computer and Information 28, no.11 (2023) : 1-11.doi: 10.9708/jksci.2023.28.11.001