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<p>In this study, a multiscale framework combining the Lifting Wavelet Transform (LWT) and multi-path Convolutional Neural Network (CNN) is proposed to enhance the analysis of histopathological breast cancer images. LWT is employed due to its capability to obtain multi-resolution features that effectively retain key textural details. In particular, a multi-path CNN facilitates the concurrent processing of features at multiple levels of wavelet decompositions, thereby preserving diagnostic information that sole-path CNN models may lose. The approach was evaluated on the BreakHis dataset, which contains 7,638 rated images at varying magnifications, achieving 99.34% test accuracy by combining magnification levels using a Haar wavelet filter. Results obtained from comparative analyses against general CNN models confirm that the new strategy performs better, thereby validating the efficacy of integrating wavelet-based textural extraction and state-of-the-art CNN models for early-stage breast cancer diagnosis.</p>

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Wavelet-CNN Feature Fusion Architecture for Robust Breast Cancer Classification in Histopathological Imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Manvi Bohra,
  • Kamred Udham Singh,
  • Indrajeet Kumar,
  • Mohd Asif Shah

摘要

In this study, a multiscale framework combining the Lifting Wavelet Transform (LWT) and multi-path Convolutional Neural Network (CNN) is proposed to enhance the analysis of histopathological breast cancer images. LWT is employed due to its capability to obtain multi-resolution features that effectively retain key textural details. In particular, a multi-path CNN facilitates the concurrent processing of features at multiple levels of wavelet decompositions, thereby preserving diagnostic information that sole-path CNN models may lose. The approach was evaluated on the BreakHis dataset, which contains 7,638 rated images at varying magnifications, achieving 99.34% test accuracy by combining magnification levels using a Haar wavelet filter. Results obtained from comparative analyses against general CNN models confirm that the new strategy performs better, thereby validating the efficacy of integrating wavelet-based textural extraction and state-of-the-art CNN models for early-stage breast cancer diagnosis.