Survey on Distributed AI-Enhanced Deep Learning for Predicting Chemo Response in Non-Hormone Receptor Breast Cancer
A. Gokulalakshmi, Tamilarasan Ananth Kumar, P. Kanimozhi · Asian Journal of Applied Science and Technology · 2023
This study offers a novel method for forecasting the response to chemotherapy in non-hormone receptor breast cancer, a difficult and complicated condition. TensorFlow-powered Spatial Temporal Integration (CNN-RNN) Architecture is used in the methods to integrate clinical data and histological images. Heuristic-driven deep learning techniques use domain-specific knowledge to build models and choose features. Using clinical knowledge, Hybrid Differential Evolution and Particle Swarm Optimization (DE-PSO) optimizes the model's parameters. Because Lime offers comprehensible justifications for the model's predictions, its adoption guarantees transparency and interpretability. Furthermore, federated learning is used in a distributed training approach to preserve scalability and safeguard patient data privacy. This method offers precision and empathy for better treatment decisions for non-hormone receptor breast cancer by fusing AI with clinical expertise.