In this work, the wavelet transformation (WT) under the context of convolution neural network (CNN) is developed and applied for breast cancer detection. The main objective is to investigate the effectiveness of the WCNN pooling architecture when compared to other two famous pooling strategies; max and average pooling, particularly targeting at the features extraction and classifying the phases of breast cancer by avoiding the under and overfitting problems. It is discovered in this work that the combination of WT and CNN outperforms the traditional and typical CNNs (with 96.49% of accuracy 95.81% of precision, 96.73% of recall and 96.23% of F measure).
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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