Focal Cosine-Enhanced EfficientNetB0: A Novel Approach to Classifying Breast Histopathological Images

Early and accurate breast cancer diagnosis is critical in enhancing patient survival rates, with histopathological image analysis serving as a key diagnostic tool. To address challenges in breast histopathology image analysis, including multi-magnification characteristics, insufficient feature extraction in traditional CNNs, and high inter-class similarity coupled with significant intra-class variation among tumor subtypes, this work proposes a focal cosine-enhanced EfficientNetB0 (FCE-EfficientNetB0) classification model. The framework incorporates a multiscale efficient attention mechanism into a multiscale efficient mobile inverted bottleneck conv, where parallel 1D convolutional branches extract features across magnification levels, while the attention mechanism prioritizes clinically relevant patterns. A focal cosine hybrid loss function further optimizes classification by enlarging interclass distances and reducing intraclass variations in the feature space. Experimental results demonstrate state-of-the-art performance, with the model achieving 99.34% accuracy for benign/malignant classification and 95.97% accuracy for eight-subtype classification on the BreakHis dataset, confirming its effectiveness in breast cancer histopathology analysis.