An ensemble of neural networks for breast cancer diagnosis
Küçük Resim Yok
Tarih
2017
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Since breast cancer is a common disease in society all over the world, early diagnosis is of vital importance in order to treat patients before it reaches an irreversible phase. Expert systems are being developed to make it easier to diagnose the disease. In this study, an ensemble of neural networks named radial basis function network (RBFN), generalized regression neural network (GRNN) and feed forward neural network (FFNN) is implemented to separate breast cancer data samples into benign/malignant classes. The utilities of these common methods and the proposed hybrid model which is a combination of these methods are explored and their performances are comparatively presented. The experimental results on Wisconsin Diagnostic Breast Cancer (WDBC) dataset have proven that the proposed method presents a promise for diagnosis of breast cancer. The proposed model can be used as a tool to assist medical specialists in making their decision on the disease. © 2017 IEEE.
Açıklama
2nd International Conference on Computer Science and Engineering, UBMK 2017 -- 5 October 2017 through 8 October 2017 -- -- 132116
Anahtar Kelimeler
Breast cancer diagnosis, Expert system, Feed forward neural network, Generalized regression neural network, Radial basis function network
Kaynak
2nd International Conference on Computer Science and Engineering, UBMK 2017
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
