A Hybrid ConvMixer–AC-RUNHHO Framework with Multi-Scale Patch Learning for Robust Breast Cancer Histopathological Image Classification

Breast cancer is a highly prevalent malignancy among women globally and arises from the uncontrolled proliferation of abnormal cells in breast tissue. Timely and precise diagnosis is critical for effective treatment and enhanced survival. Histopathological image analysis is considered the gold standard; nevertheless, manual assessment is labor-intensive and prone to variability. Existing deep learning and transformer-based approaches demonstrate strong effectiveness; however, they incur significant computational complexity and limited efficiency in capturing multi-scale features. To address these challenges, this research presents a framework that integrates ConvMixer, multi-scale patch learning, and an Adaptive Combined Runge–Kutta–Harris Hawks Optimization (AC-RUNHHO) algorithm. The model effectively captures both fine-grained cellular patterns and global tissue structures, while adaptive optimization improves convergence and hyperparameter tuning. The framework is evaluated on a breast cancer histology dataset comprising 4000 histopathological images across four classes. Experimental results demonstrate robust performance under the evaluated experimental conditions, achieving 98.63% accuracy, 98.63% precision, 98.62% recall, and 98.62% F1-score. Ablation and cross-validation analyses further confirm the generalization capability of the model. Overall, the developed framework demonstrates promising performance in computer-aided breast histopathological image classification, achieving high predictive accuracy and providing interpretable visual explanations.