Next Article in Journal
Visual Interface Evaluation for Wearables Datasets: Predicting the Subjective Augmented Vision Image QoE and QoS
Previous Article in Journal
A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
Article Menu

Export Article

Open AccessArticle
Future Internet 2017, 9(3), 39; doi:10.3390/fi9030039

Azure-Based Smart Monitoring System for Anemia-Like Pallor

Department of Electrical and Computer Engineering,University of Washington, Bothell, WA 98011, USA
*
Author to whom correspondence should be addressed.
Received: 26 June 2017 / Revised: 20 July 2017 / Accepted: 23 July 2017 / Published: 26 July 2017
View Full-Text   |   Download PDF [3319 KB, uploaded 28 July 2017]   |  

Abstract

Increasing costs of diagnostic healthcare have necessitated the development of hardware independent non-invasive Point of Care (POC) systems. Although anemia prevalence rates in global populations vary between 10% and 60% in various demographic groups, smart monitoring systems have not yet been developed for screening and tracking anemia-like pallor. In this work, we present two cloud platform-hosted POC applications that are directed towards smart monitoring of anemia-like pallor through eye and tongue pallor site images. The applications consist of a front-end graphical user interface (GUI) module and two different back-end image processing and machine learning modules. Both applications are hosted on a browser accessible tomcat server ported to an Azure Virtual Machine (VM). We observe that the first application spatially segments regions of interest from pallor site images with higher pallor classification accuracy and relatively longer processing times when compared to the lesser accurate yet faster second application. Also, both applications achieve 65%–98% accuracy in separating normal images from images with pallor or abnormalities. The optimized front-end module is significantly light-weight with a run-through time ratio of 10−5 with respect to the back-end modules. Thus, the proposed applications are portable and hardware independent, allowing for their use in pallor monitoring and screening tasks. View Full-Text
Keywords: point of care; azure; diagnostics; screening; classification point of care; azure; diagnostics; screening; classification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Roychowdhury, S.; Hage, P.; Vasquez, J. Azure-Based Smart Monitoring System for Anemia-Like Pallor. Future Internet 2017, 9, 39.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top