# Azure-Based Smart Monitoring System for Anemia-Like Pallor
## 1 Introduction  
Point of Care (POC) testing applications enable medical diagnostic applications to be provided at the patient end for resourceful treatment and follow-up care. Additionally, m-health applications supported by mobile devices have become increasingly popular over the past decade to promote \"telemedicine\" systems, where the patient and medical care provider are not collocated in the same geographical location [1]. With the 4-6% annually increasing costs of healthcare in USA [2], the need for POC and m-health technologies for faster diagnostic purposes have become necessary. In this work two different approaches towards providing patient-end POC diagnostic screening functionality through cloud-hosted applications are presented. Also, the time complexity analysis and diagnostic accuracy of a MATLAB based back-end versus a Java based back-end are compared, and an optimal approach towards the design of a lightweight front-end is highlighted. The design of POC testing applications is limited by constraints such as: ease of use, speed, data integrity, reliability and repeatability. Here, the reliability and repeatability of the applications are dependent on the back-end processing system specifications that in turn are dictated by user preferences between speed versus accuracy of screening. Thus, Microsoft Azure cloud based services can be used as viable resources to provide device independent web-service calls while maintaining data security and privacy [3]; all the necessary components for successful hosting of a medical based diagnostics system. In our prior pilot study [4], we analyzed color-planes and gradient-based features for their discriminating capabilities towards facial pallor site images. We had observed that hierarchicalclassification for pallor severity, using boosted decision trees and decision forest, was the most successful strategy for regional-feature-based automated pallor detection. In this work, we perform additional analysis of pallor specific features in dedicated pallor site images by assessing the activated feature maps (AFMs) from trained convolutional neural network (CNN) models [5; 6]. Our analysis of AFMs indicate that pallor specific regions of interest are inline with domain knowledge that blood vessel densities in the conjunctiva region of eyes and inner tongue regions are most indicative of anemia-like pallor. Additionally, in this work, we combine the image-processing/classification modules with front-end image collection and processing modules that are hosted on the Microsoft Azure cloud-based platform for designing complete web-based smart health monitoring applications. At present, computer aided diagnostics (CAD) and POC applications are aimed at providing quick \"expert\" diagnostics for screening and resourcefulness of treatment and care-delivery protocols. Some well-known CAD systems include screening systems for diabetic retinopathy [7], hypertensive risk [8] and pathology detected using digital chest radiographs [9]. However, no screening system has been developed for anemia-related risk assessment till date. This work is aimed at utilizing facial pallor site images with varying severities, such as close-up images from eyes and tongue for anemia-like medical screening applications as shown in Figure 1. It is noteworthy that the pallor-specific features under analysis in this work are patient demographics independent, motivated by domain knowledge and focused on regions signifying the flushed appearance of underlying blood vessels as opposed to skin-based pallor detections. This paper makes three key contributions. First, it presents the flow for front-end and back-end communications towards the design of non-invasive medical POC testing applications. The proposed applications are ideal for uploading patient images, such as facial pallor site images, and storing them in the cloud while invoking a back-end system that processes the uploaded images and detects the presence of anemia-like pallor.