Azure-Based Smart Monitoring System for Anemia-Like Pallor

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

Azure-Based Smart Monitoring System for Anemia-Like Pallor

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