CAE-StackedNet: A Hybrid Convolutional Autoencoder and Stacked Ensemble Framework for Histopathological Breast Cancer Image Classification

The accurate classification of breast cancer histopathology images is critical for early diagnosis, risk stratification, and treatment planning. However, traditional supervised learning approaches are hindered by the limited availability of annotated medical datasets. At the same time, single-model classifiers often struggle to generalize effectively across subtle morphological variations in tissue structures. To address these challenges, this study proposes CAE-StackNet, a hybrid framework that combines self-supervised feature learning via a Convolutional Autoencoder (CAE) with a heterogeneous stacked-ensemble classifier. The CAE is first trained to reconstruct breast cancer histopathology images from the BACH dataset, enabling it to learn compact, morphology-preserving features without requiring class labels. These self-learned features are then classified using a stacked ensemble of Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), with a meta-learner aggregating their predictions to produce the final diagnosis. Experimental results demonstrate that CAE-StackNet achieves an AUC of 0.99 and an average precision of 0.96, outperforming individual classifiers trained on the same features. MLP achieved the highest standalone accuracy (0.89), but the stacked ensemble exhibited superior discriminative power, reflecting the advantage of combining complementary classifiers in a diverse ensemble framework. For breast cancer classification in histopathology, CAE-StackNet provides a data-efficient and clinically meaningful solution by reducing the need for manual annotation and improving robustness through ensemble learning. The developed method enables more reproducible and objective histopathological grading and has excellent potential for incorporation into computer-aided diagnostic (CAD) systems. To increase clinical interpretability and gain the trust of pathologists, future efforts will involve external validation on multi-center datasets, integrating pathology-aware constraints into the CAE training process, and creating explainability tools. Recent transformer‑based frameworks, such as DFViT, fuse convolutional and vision‑transformer blocks to capture both local and global tissue patterns and have achieved outstanding accuracy on the BACH and BreakHis datasets. By contrast, CAE‑StackNet achieves comparable AUC and average precision while relying primarily on self‑supervised features and requiring far fewer annotated images, underscoring its potential for scalable clinical deployment.