SHS Web Conf.
Volume 102, 2021The 3rd ETLTC International Conference on Information and Communications Technology (ETLTC2021)
|Number of page(s)||5|
|Section||Applications in Computer Science|
|Published online||03 May 2021|
Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System
Adaptive Systems Laboratory, School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Japan
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Recent years have witnessed a rapid growth of Artificial Intelligence (AI) in biomedical fields. However, an accurate and secure system for pneumonia detection and diagnosis is urgently needed. We present the optimization and implementation of a collaborative learning algorithm for an AI-Enabled Real-time Biomedical System (AIRBiS), where a convolution neural network is deployed for pneumonia (i.e., COVID-19) image classification. With augmentation optimization, the federated learning (FL) approach achieves a high accuracy of 95.66%, which outperforms the conventional learning approach with an accuracy of 94.08%. Using multiple edge devices also reduces overall training time.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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