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Xiaoli Wang¹, Guowei Wang¹, Luhan Li²

¹Electronics Information Engineering College, Changchun University, Changchun 130022, China.

|November 26, 2025

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A novel Multi-Feature Fusion Classification Network (MFF-ClassificationNet) improves breast cancer diagnosis by integrating local and global image features. This AI approach enhances accuracy in classifying histopathological images, aiding early detection and reducing mortality.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer is a leading cause of death globally among women.
Accurate and early diagnosis of breast cancer is critical for improving patient outcomes.
Histopathological image analysis is key for breast cancer diagnosis.

Purpose of the Study:

To introduce a Multi-Feature Fusion Classification Network (MFF-ClassificationNet) for enhanced breast histopathological image classification.
To improve the accuracy and robustness of computer-aided diagnosis systems for breast cancer.

Main Methods:

Developed a two-branch parallel network combining a Convolutional Neural Network (CNN) for local features and a Transformer for global dependencies.
Implemented a Multi-Feature Fusion module with a Convolutional Block Attention Module-Squeeze and Excitation (CBAM-SE) fusion block.

Keywords:

attention mechanism breast cancer histopathological images multi-feature fusion transformer

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Utilized residual inverted multilayer perceptron for fine-grained feature representation and category-specific lesion characterization.

Main Results:

Achieved high accuracies on the BreakHis dataset: 98.30% (40×), 97.62% (100×), 98.81% (200×), and 96.07% (400×).
Obtained an accuracy of 97.50% on the BACH dataset.
Demonstrated superior performance compared to conventional single-path approaches by effectively integrating multi-scale and context-aware information.

Conclusions:

The MFF-ClassificationNet effectively integrates local and global features for superior breast cancer classification.
The proposed network offers a robust and generalizable framework for advancing computer-aided diagnosis of breast cancer.
This approach has the potential to significantly improve early detection rates and reduce breast cancer mortality.