The purpose of this paper is to propose and study the structure of wavelet transformation (WT) and convolution neural networks (CNN). To get more insights into its effectiveness, three WCNN architectures are designed and tested against one another seeking which model provides the best performance in breast cancer detection using histopathological images. The Breast cancer histopathological database (BreakHis) is used for this task.
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