Diagnosing Clinical Diseases using an Edge-Enabled Deep Learning Technology

Abstract

Along with the development of high-speed communication networks, edge-enabled mobile devices have opened new possibilities for diagnosing health conditions or developing suitable treatment plans. While the latest deep learning technology has deployed to restructure and translate complex medical applications, the costly training operation using large-scale neural networks with tremendous amount of data remain the major challenge. In this work, we take advantages of reservoir computing to develop a reliable and low-cost medical diagnostic system for edge-enabled devices. Specifically, an echo state network (ESN) was trained to discover non-obvious correlation and likelihood from biomedical data with respect to various patients. Through the determination of cardiovascular and coronavirus diseases, numerical evaluations demonstrated advantage of ESN against the state-of-the-art. At particularly no computation overhead, ESN precisely described the prediction tasks of health conditions, offering improvements of up to 1000x in sample reduction, 175x in training speedup, and 15 percentage points in prediction accuracy.