A Survey of Methods and Architectures for an Efficient Cascaded Deep Capsule Cell Attention Network Model for Breast Cancer Molecular Subtypes Prediction Using Mammogram Images

Abstract

Breast cancer remains one of the leading causes of mortality among women worldwide, with molecular subtype identification playing a critical role in determining personalized treatment strategies. Traditional diagnostic approaches, such as immunohistochemistry, are invasive, time-consuming, and prone to sampling errors. In recent years, deep learning-based techniques have emerged as promising alternatives for non-invasive subtype prediction using mammogram images. This paper presents a comprehensive survey of methods and architectures focusing on advanced deep learning frameworks, particularly cascaded capsule networks integrated with attention mechanisms, for accurate breast cancer molecular subtype classification. Capsule networks demonstrate superior capability in preserving spatial hierarchies compared to conventional convolutional neural networks, while attention modules enhance feature representation by focusing on clinically relevant regions. Recent studies highlight that attention-guided architectures, such as DenseNet-CBAM and hybrid attention networks, significantly improve classification accuracy and interpretability . Furthermore, advancements in hybrid and ensemble models combining handcrafted and deep features have shown improved robustness across diverse datasets . This survey analyzes recent literature, identifies key challenges such as data imbalance, interpretability, and computational complexity, and proposes future directions for developing efficient cascaded deep capsule attention networks. The findings emphasize the potential of integrating capsule structures with attention mechanisms to achieve accurate, scalable, and clinically applicable breast cancer subtype prediction systems.