Graph-Based Sparse Diagnostic Path Routing for Breast Cancer Detection Using Histopathological

Accurately interpreting breast histopathological images continues to be a challenge due to the high structural heterogeneity and redundant feature interactions across different magnification levels. To tackle the problem, the study presents a Sparse Diagnostic Path Aggregation (SDPA) framework for binary classification of breast cancer from histopathology images. The proposed method builds a feature interaction graph from deep convolutional representations and uses a learnable sparse routing mechanism to selectively activate diagnostically relevant paths while redundant feature connections are suppressed. Finally, a path, level aggregation strategy is employed to integrate the most compelling diagnostic evidence for the final classification. The experiments on the BreakHis histopathological dataset in the binary classification setting (benign vs. malignant) reveal that the proposed frame work is capable of achie ving a classification accuracy of 97.4%, with 97.8% precision, 97.1% recall,$97.4 \% ~\mathrm{F} 1$, score, and an AUC of 0.989. Besides the standard performance measures, routing, specific assessment attests to the efficiency and stability of the sparse diagnostic reasoning process, resulting in an Active Path Ratio of 0.27$\pm 0.04$, Routing Entropy of$1.18 \pm 0.09$, Information Retention Ratio of$0.91 \pm 0.03$, and Path Consistency Across Magnifications of$0.84 \pm 0.05$. The superior classification accuracy of the proposed SDPA framework is evidently supported by the results obtained. The frame work is able to select diagnostic paths that are interpretable, consistent, and information, efficient. Therefore, it is characterized as a dependable tool for computer, aided breast cancer diagnos is.