Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System
Keyword(s):
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.
2019 ◽
Vol 34
(11)
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pp. 4924-4931
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2012 ◽
Vol 468-469
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pp. 11-21
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2020 ◽
Vol 2674
(11)
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pp. 220-234
2012 ◽
pp. 171-177
Keyword(s):
2013 ◽
Vol 433-435
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pp. 1388-1391
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