Wavelet-Convolutional Neural Network: An Improved Deep Learning Model for Breast Cancer Detection from Histopathology Images

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Article Type:
Research/Original Article (دارای رتبه معتبر)
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) isproposed, incorporating wavelet filtersand convolutional filtersin each layer to capture both frequency and spatial domain features. The processed images resulting from both types of filters werecombined and passed through a MaxPooling layer to extract salient features.Four such hybrid layers were considered for extracting effective features.This novel approach allowedthe 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 demonstratedthe potential of combining wavelet and convolutional filters for improved breast cancer detection, offering a promising approach for early diagnosis and better patient outcomes

Language:
English
Published:
Archives of Breast Cancer, Volume:12 Issue: 1, Feb 2025
Pages:
73 to 84
https://www.magiran.com/p2825970