Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

ABSTRACT: Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, thro

SUBMITTER: Shokr A 

PROVIDER: S-EPMC8299938 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature