<![CDATA[Breast cancer detection through histopathological imaging remains challenging due to complex tissue morphology, observer variability, and subtle differences between invasive and pre-invasive lesions. Conventional computer-aided diagnostic systems often rely on single-domain feature extraction, restricting multi-scale representation and clinical interpretability. To overcome these limitations, we propose a verified diagnostic framework integrating five analytical components for efficient and explainable breast cancer classification. The adaptive multi-level histopathological feature selection using cross-domain mutual information maximization (AMFSCDMIM) extracts highly informative morphological and frequency features with minimal redundancy. The deep hierarchical hybrid morphological– frequency encoding network (DH-HMFEN) refines spatial–spectral representations, while the multi-scale morphological attention classification network (MS-MACNet) applies adaptive attention across tissue structures for improved discrimination. The adaptive ensemble validation for breast cancer classification (AEV-BCC) calibrates confidence levels for enhanced reliability, and the comparative analytical performance validation with interpretability integrated metrics (CAPV-IIM) quantitatively evaluates model explainability using expert annotations. Experimental results on benchmark datasets achieve 96% accuracy, 0.98 area under the receiver operating characteristic curve (AUROC), and a 0.88 interpretability alignment score, outperforming existing methods. The proposed confidence-calibrated, multi-domain, and multi-scale framework enhances diagnostic precision and clinical trust in histopathology-based breast cancer detection.]]>
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