Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey

TL;DR

This comprehensive survey reviews the state of edge deep learning in computer vision and medical diagnostics, highlighting technical principles, hardware categorization, implementation methods, practical applications, and future challenges.

Contribution

It introduces a novel categorization of edge hardware platforms and discusses effective implementation strategies for deep neural networks on edge devices.

Findings

Edge deep learning enables real-time decision making in medical diagnostics.

Hardware platform categorization improves operational effectiveness.

Model compression techniques facilitate deployment on resource-constrained devices.

Abstract

Edge deep learning, a paradigm change reconciling edge computing and deep learning, facilitates real-time decision making attuned to environmental factors through the close integration of computational resources and data sources. Here we provide a comprehensive review of the current state of the art in edge deep learning, focusing on computer vision applications, in particular medical diagnostics. An overview of the foundational principles and technical advantages of edge deep learning is presented, emphasising the capacity of this technology to revolutionise a wide range of domains. Furthermore, we present a novel categorisation of edge hardware platforms based on performance and usage scenarios, facilitating platform selection and operational effectiveness. Following this, we dive into approaches to effectively implement deep neural networks on edge devices, encompassing methods such as lightweight design and model compression. Reviewing practical applications in the fields of computer vision in general and medical diagnostics in particular, we demonstrate the profound impact edge-deployed deep learning models can have in real-life situations. Finally, we provide an analysis of potential future directions and obstacles to the adoption of edge deep learning, with the intention to stimulate further investigations and advancements of intelligent edge deep learning solutions. This survey provides researchers and practitioners with a comprehensive reference shedding light on the critical role deep learning plays in the advancement of edge computing applications.