Breast Cancer Classification Using Graph Convolutional Networks and DenseNet121 with Pruning

Breast cancer remains a significant global health burden, particularly among women, necessitating accurate and accessible diagnostic systems for early detection and intervention. Despite advancements in medical technology, challenges persist such as high dimensionality, variability, and complexity. Hence, this study introduces a novel approach that amalgamates DenseNe5t121 architecture within graph convolutional networks (GCNs) for breast cancer classification. The proposed method combines the strengths of a pre-trained DenseNet121 model, pruning techniques, and GCN with a cross-space filter (CSF). DenseNet121 is employed for feature extraction, while pruning enhances efficiency and prevents overfitting. GCNs capture structural and semantic information from the data and the CSF aids in regularization. Furthermore, the incorporation of the CSF enhances model performance by effectively integrating information from both graph topology and node attributes. The proposed approach was trained and evaluated using the BreakHis dataset, a widely recognized resource for breast cancer classification through histopathological images. By leveraging several performance measures consisting of precision, recall, F1-score, AUC, MCC, and accuracy, the proposed approach was compared against existing models to assess its effectiveness. The results indicate that the proposed method, particularly when combined with the CSF, outperforms existing approaches, demonstrating its potential as an advanced tool for accurate and interpretable breast cancer diagnosis.