An ensemble of neural networks for breast cancer diagnosis

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 network...

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Veröffentlicht in:2nd International Conference on Computer Science and Engineering : Antalya-Türkiye 5-8 Ekim (October) 2017 S. 538 - 543
Hauptverfasser: Yavuz, Erdem, Eyupoglu, Can, Sanver, Ufuk, Yazici, Rifat
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2017
Schlagworte:
Biological neural networks
Breast cancer
breast cancer diagnosis
expert system
Expert systems
feed forward neural network
generalized regression neural network
Neurons
radial basis function network
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Zusammenfassung: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.
DOI:10.1109/UBMK.2017.8093456