FastDDS-Based Middleware System for Remote X-Ray Image Classification Using Raspberry Pi

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Bibliographische Detailangaben
Veröffentlicht in:	arXiv.org (Dec 10, 2024), p. n/a
1. Verfasser:	Khater, Omar H
Weitere Verfasser:	Almadani, Basem, Aliyu, Farouq
Veröffentlicht:	Cornell University Library, arXiv.org
Schlagworte:	Image classification Internet of Things Remote regions Neural networks Health services Real time Middleware Network latency
Online-Zugang:	Citation/Abstract Full text outside of ProQuest
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Beschreibung
Abstract:	Internet of Things (IoT) based healthcare systems offer significant potential for improving the delivery of healthcare services in humanitarian engineering, providing essential healthcare services to millions of underserved people in remote areas worldwide. However, these areas have poor network infrastructure, making communications difficult for traditional IoT. This paper presents a real-time chest X-ray classification system for hospitals in remote areas using FastDDS real-time middleware, offering reliable real-time communication. We fine-tuned a ResNet50 neural network to an accuracy of 88.61%, a precision of 88.76%, and a recall of 88.49\%. Our system results mark an average throughput of 3.2 KB/s and an average latency of 65 ms. The proposed system demonstrates how middleware-based systems can assist doctors in remote locations.
ISSN:	2331-8422
Quelle:	Engineering Database