Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System

Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System

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 Rea...

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Main Authors: Phea Sinchhean, Wang Zhishang, Wang Jiangkun, Ben Abdallah Abderazek
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:SHS Web of Conferences
Online Access:https://www.shs-conferences.org/articles/shsconf/pdf/2021/13/shsconf_etltc2021_04017.pdf
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spelling doaj-b3269e99c56a4d6887fc5cf95b423e462021-05-04T12:25:01ZengEDP SciencesSHS Web of Conferences2261-24242021-01-011020401710.1051/shsconf/202110204017shsconf_etltc2021_04017Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical SystemPhea Sinchhean0Wang Zhishang1Wang Jiangkun2Ben Abdallah Abderazek3Adaptive Systems Laboratory, School of Computer Science and Engineering, University of AizuAdaptive Systems Laboratory, School of Computer Science and Engineering, University of AizuAdaptive Systems Laboratory, School of Computer Science and Engineering, University of AizuAdaptive Systems Laboratory, School of Computer Science and Engineering, University of AizuRecent 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.https://www.shs-conferences.org/articles/shsconf/pdf/2021/13/shsconf_etltc2021_04017.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Phea Sinchhean
Wang Zhishang
Wang Jiangkun
Ben Abdallah Abderazek
spellingShingle Phea Sinchhean
Wang Zhishang
Wang Jiangkun
Ben Abdallah Abderazek
Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System
SHS Web of Conferences
author_facet Phea Sinchhean
Wang Zhishang
Wang Jiangkun
Ben Abdallah Abderazek
author_sort Phea Sinchhean
title Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System
title_short Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System
title_full Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System
title_fullStr Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System
title_full_unstemmed Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System
title_sort optimization and implementation of a collaborative learning algorithm for an ai-enabled real-time biomedical system
publisher EDP Sciences
series SHS Web of Conferences
issn 2261-2424
publishDate 2021-01-01
description 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.
url https://www.shs-conferences.org/articles/shsconf/pdf/2021/13/shsconf_etltc2021_04017.pdf
work_keys_str_mv AT pheasinchhean optimizationandimplementationofacollaborativelearningalgorithmforanaienabledrealtimebiomedicalsystem
AT wangzhishang optimizationandimplementationofacollaborativelearningalgorithmforanaienabledrealtimebiomedicalsystem
AT wangjiangkun optimizationandimplementationofacollaborativelearningalgorithmforanaienabledrealtimebiomedicalsystem
AT benabdallahabderazek optimizationandimplementationofacollaborativelearningalgorithmforanaienabledrealtimebiomedicalsystem
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