Abstract
In this article, we share our experiences in designing and developing a suite of deep neural network-(DNN) based COVID-19 case detection and recognition framework. Existing pathological tests such as RT-PCR-based pathogen RNA detection from nasal swabbing seem to display low detection rates during the early stages of virus contraction. Moreover, the reliance on a few overburdened laboratories based around an epicenter capable of supplying large numbers of RT-PCR tests makes this testing method non-scalable when the rate of infections is high. Similarly, finding an effective drug or vaccine with which to combat COVID-19 requires a long time and many clinical trials. The development of pathological COVID-19 tests is hindered by shortages in the supply chain of chemical reagents necessary for testing on a large scale. This diminishes the speed of diagnosis and the ability to filter out COVID-19 positive patients from uninfected patients on a national level. Existing research has shown that DNN has been successful in identifying COVID-19 from radiological media such as CT scans and X-ray images, audio media such as cough sounds, optical coherence tomography to identify conjunctivitis and pink eye symptoms on the ocular surface, body temperature measurement using smartphone fingerprint sensors or thermal cameras, the use of live facial detection to identify safe social distancing practices from camera images, and face mask detection from camera images. We also investigate the utility of federated learning in diagnosis cases where private data can be trained via edge learning. These point-of-care modalities can be integrated with DNN-based RT-PCR laboratory test results to assimilate multiple modalities of COVID-19 detection and thereby provide more dimensions of diagnosis. Finally, we will present our initial test results, which are encouraging.