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

2026-05-06

AI Summary

This study addresses the challenges of deploying real-time computer vision and medical diagnosis on resource-constrained edge devices by providing a systematic review of key techniques in edge deep learning. It proposes a novel taxonomy of edge hardware platforms based on performance characteristics and application scenarios, and integrates methodologies including lightweight network design, model compression, and inference optimization. The work establishes a comprehensive technical roadmap for edge intelligence tailored to medical diagnostics, offering theoretical guidance and practical insights for platform selection and system deployment through representative case studies, thereby facilitating the adoption of intelligent edge systems in critical domains such as healthcare.

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.

Problem

• edge deep learning
• computer vision
• medical diagnostics
• edge computing
• deep neural networks

Innovation

• edge deep learning
• model compression
• lightweight neural networks
• edge hardware categorization
• medical diagnostics