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- Title
Breast Cancer Classification using a Hybrid Model of Fuzzy and Neural Network.
- Authors
Wutsqa, Dhoriva Urwatul; Abadi, Agus Maman; Nurhayadi
- Abstract
In this study, breast cancer has been successfully classified by using a hybrid model of fuzzy and neural network. For this purpose, we propose two-hybrid models namely fuzzy neural network (fuzzy NN) and fuzzy radial basis function neural network (fuzzy RBFNN). The backpropagation algorithm is employed to estimate the weights of the fuzzy NN model. The K-Means clustering and singular value decomposition are developed to estimate the parameters of the fuzzy RBFNN. The benign and malignant breast tissues data drawn from Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used in the classification. The variables in the data sets are the features from the digitized images of fine needle aspiration (FNA) biopsy of the breast. The result shows that both models deliver high accuracies on WBCD and WDBC data sets. However, the fuzzy NN shows slightly better performance than the fuzzy RBFNN.
- Subjects
WISCONSIN; FUZZY neural networks; ARTIFICIAL neural networks; BREAST; TUMOR classification; BREAST cancer; SINGULAR value decomposition
- Publication
IAENG International Journal of Computer Science, 2022, Vol 49, Issue 2, p550
- ISSN
1819-656X
- Publication type
Article