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Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest

Mahbub Ul Alam1, Rahim Rahmani1

  • 1Department of Computer and Systems Sciences, Stockholm University, 16407 Stockholm, Sweden.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary

This study evaluated machine learning for COVID-19 detection and lung segmentation using chest X-rays on edge devices (Raspberry Pi) versus servers. Edge devices excelled in lung segmentation, while servers were better for COVID-19 detection.

Keywords:
federated learninginternet of medical thingsmulti-task learningsemi-supervised machine learningtransfer learning

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Internet of Medical Things (IoMT)

Background:

  • The Internet of Medical Things (IoMT) enables advanced automatic medical decision support systems by integrating medical devices and data.
  • Chest radiography is crucial for diagnosing respiratory conditions like COVID-19 and for anatomical segmentation.

Purpose of the Study:

  • To investigate the efficacy of various machine learning techniques for COVID-19 detection and lung segmentation using chest X-rays.
  • To compare the performance of edge computing (Raspberry Pi) versus server-centric approaches within an IoMT framework.

Main Methods:

  • Exploration of federated learning, semi-supervised learning, transfer learning, and multi-task learning.
  • Implementation and evaluation on Raspberry Pi devices for edge computing scenarios.
  • Server-centric simulation for comparative analysis.
  • Performance metrics included accuracy, precision, recall, F-score for COVID-19 detection, and average dice score for lung segmentation.

Main Results:

  • Raspberry Pi-centric devices demonstrated superior performance in lung area segmentation detection.
  • Server-centric experiments yielded better results for COVID-19 detection.
  • The study highlights the trade-offs between edge and server-centric processing in IoMT decision support.

Conclusions:

  • IoMT systems integrating medical data and decision support tools can benefit all stakeholders.
  • Edge devices show promise for specific tasks like lung segmentation, while centralized servers remain advantageous for others, such as COVID-19 detection.