Comprehensive Review of Deep Learning Algorithms for Embedded Healthcare Applications
Year: 2024
Type: article
Abstract: The integration of deep learning algorithms into embedded healthcare applications has emerged as a promising avenue for revolutionizing medical diagnostics, monitoring, and treatment. This review explores the performance, suitability, and implications of various deep learning algorithms within the context of embedded healthcare systems. Leveraging a diverse range of algorithms including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Transformer Networks, and more. The study presents an overview of embedded systems in healthcare, which leverage Deep learning algorithms to enhance their performance and enable physicians to provide prompt and accurate responses to patients.
Author: Hari Krishnan Andi
Institution: Asia Metropolitan University
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403402560