Globally, breast cancer is considered as a major cause of cancer related illness and death among women. Hence, it requires an early and precise diagnosis system to lower the mortality rate. The conventional deep learning models pose privacy and security concerns under healthcare regulations. These issues are addressed by a novel Federated Flash Attention Hierarchical Deep Capsule Network model for robust detection and classification. The proposed model is based on three factors such as Hierarchical Deep Capsule Network, which models spatial and structural relationships within mammographic features, Flash Attention mechanism to improve diagnostic focus, and Federated Learning, which enables multi-institutional collaboration without transferring raw data. The privacy preservation in the proposed framework is achieved through federated learning, where raw data is not shared between clients. The model is evaluated on benchmark mammography datasets using an 80:20 training-testing split and compared with existing machine learning and federated approaches. The experimental analysis is conducted using different measures to demonstrate improved performance compared to baseline models. The proposed Federated Flash Attention Hierarchical Deep Capsule Network attains an accuracy of 98.89%, a precision of 98.78%, a recall of 98.51%, an F1-score of 98.64%, and a Specificity of 98.19%.
Venkatachalam et al. (Sun,) studied this question.