Deep Learning in Healthcare: Paradigms and Applications

by Yen-Wei Chen (Editor), Lakhmi C. Jain (Editor)

Overview

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems.

Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data.

Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

Product Details

ISBN-13:	9783030326050
Publisher:	Springer International Publishing
Publication date:	11/19/2019
Series:	Intelligent Systems Reference Library , #171
Edition description:	1st ed. 2020
Pages:	218
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Medical Image Detection Using Deep Learning.- Medical Image Segmentation Using Deep Learning.- Medical Image Classification Using Deep Learning.