Survey on Distributed AI-Enhanced Deep Learning for Predicting Chemo Response in Non-Hormone Receptor Breast Cancer

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...

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Bibliographic Details
Published inAsian Journal of Applied Science and Technology Vol. 7; no. 4; pp. 27 - 34
Main Authors A. Gokulalakshmi, kumar, Dr. T. Ananth, Dr. P. Kanimozhi
Format Journal Article
LanguageEnglish
Published 2023
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Summary: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.
ISSN:2456-883X
2456-883X
DOI:10.38177/ajast.2023.7404