Wavelet-Convolutional Neural Network: An Improved Deep Learning Model for Breast Cancer Detection from Histopathology Images WCNN Breast Cancer
Abstract
Background: Invasive ductal carcinoma (IDC) is a prevalent type of breast cancer with significant mortality rates. Early detection is crucial for effective treatment options. Deep learning techniques have shown promise in medical image analysis, but further improvements are needed.
Methods: A Wavelet-Convolutional Neural Network (WCNN) is proposed, incorporating wavelet filters and convolutional filters in each layer to capture both frequency and spatial domain features. The processed images resulted from both types of filters are combined and passed through a MaxPooling layer to extract salient features. Four such hybrid layers are considered for extracting effective features. This novel approach allows the model to effectively learn multi-scale representations, leading to improved performance in breast cancer classification tasks. The model was trained and evaluated on a publicly available breast histopathology image dataset.
Results: The proposed WCNN achieved a classification accuracy of 98.4% for breast cancer detection, outperforming existing state-of-the-art models.
Conclusion: The WCNN framework demonstrates the potential of combining wavelet and convolutional filters for improved breast cancer detection, offering a promising approach for early diagnosis and better patient outcomes.
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