Mobile Image Analysis for Urinalysis Strips Using Backpropagation Neural Network


Tang C. (first author). In Preparation. “Mobile Image Analysis for Urinalysis Strips Using Backpropagation Neural Network.” JMIR mHealth and uHealth .


Background Urine analysis has great potential in personalized care, considering either its biological richness or its capacity to be a convenient and cost-effective medium for continuous health monitoring. Involved diagnostics include, but not limited to, urinary tract infection, kidney function, diabetes, pregnancy, and hydration testing. Smartphone and portable (or wearable) devices incorporate image sensors, offering a practical, accurate, and low-cost solution for initial self-diagnosis of disease, self-monitoring of health conditions, and preliminary examinations. This can help to develop new mHealth applications (app).
Objectives This study aims to (1) develop a mHealth app calling a model based on backpropagation (BP) neural network we proposed for urinalysis strips image analysis, then (2) evaluate the feasibility of embedding model parameters to give consumers control personal data by image processing on mobile devices.
Methods We proposed a novel BP neural network-based model to identify color similarity in these images shot by smartphone users and a standard colorimetric card. Our dataset contains 5,620 labeled urinalysis strip images. We chose four existing image recognition models as the baselines. We designed two versions of the apps for our evaluation purpose. One is a normal informed consent-based personal data collector to have users’ data for image processing on the server. The other is embedded model parameters to achieve mobile image analysis.
Results We experimented with our proposed model on our labeled dataset by randomly selecting two-third of these images as training data and the rest as testing data. The results indicate that our model performs much better than all baselines in a total of 5 testing items, with a maximum improvement rate of 28.2% and an average of 16.9%. We evaluated the two versions of apps by a sub dataset (457 urinalysis strip images). The findings demonstrate the accuracy, efficiency, and consistency of the two are similar.
Conclusions While the rich new streams of data have made it possible to tackle complex challenges in fields such as health care, we should be open about our data practices on new smart, connected products to ensure individuals’ privacy choose. It is feasible to facilitate both parties benefit from personal data collection via app design.  
Last updated on 03/30/2021