Breast cancer detection using BCDNet convolutional neural network

Breast cancer detection from histopathological images has benefited significantly from advances in deep learning; however, many high-performing models rely on deep architectures and substantial computational resources, limiting their applicability in resource-constrained clinical settings. Recent studies have demonstrated that convolutional neural networks (CNNs) can effectively support invasive ductal carcinoma (IDC) classification, but often at the cost of increased training time and memory usage 1, 13. In this study, we propose BCDNetUpdated, an optimized lightweight CNN derived from the reference BCDNet architecture 1 for IDC detection. The proposed model introduces targeted architectural refinements aimed at improving classification performance while preserving computational efficiency. Experiments conducted on the Kaggle IDC dataset demonstrate that BCDNetUpdated achieves improved accuracy, recall, and F1-score compared to the reference BCDNet, while requiring significantly less GPU memory and comparable training time. While the evaluation is limited to a single dataset and does not include formal statistical significance testing or explainability analysis, the results indicate that careful optimization of lightweight CNN architectures can yield meaningful performance improvements without increasing computational cost. Future work will incorporate multi-dataset validation, statistical rigor through k-fold cross-validation, and explainability mechanisms (Grad-CAM, attention visualization) to support clinical adoption. This work contributes to ongoing efforts toward efficient deep learning models for histopathological image analysis and provides a foundation for future studies involving expanded validation and model interpretability.