Breast cancer remains one of the most prevalent and life-threatening malignancies worldwide, wherein accurate and early diagnosis plays a pivotal role in improving patient outcomes. The screening is commonly done by traditional means, which tend to be time-consuming and labor-intensive. The detection relies on expert review, sometimes leading to issues of subjectivity, delayed diagnosis, and treatment.

This study presents a novel Deep Learning (DL) approach, designed to improve texture-structure understanding for breast cancer classification. In the proposed approach, Res-MorphNet synergistically combines ResNet with a Morphological Texture Encoding (MTE) module to extract intricate morphologic textures, while Swin-CPSANet leverages the Swin Transformer and a Cross-Patch Spatial Aggregation (CPSA) block to enhance global context understanding and feature interaction. To the best of our knowledge, this is the first attempt to integrate morphological texture encoding with cross-patch attention within a unified CNN–Transformer fusion framework for histopathological breast cancer classification.

Evaluation on the BACH dataset indicates that the proposed model achieves an accuracy of 95%, a precision of 95.49%, a recall of 95%, and an F1 score of 95.05%. These results outperform traditional CNN- and transformer-based baseline models, demonstrating the effectiveness of combining morphological texture encoding with cross-patch attention in a unified framework.

The findings indicate that the proposed architecture achieved efficient and well-balanced classification performance across multiple breast tissue classes, underscoring its potential as a valuable tool to assist in clinical diagnosis.