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Springer Deep Learning in Healthcare: Paradigms and Applications

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Description

This comprehensive guide explores deep learning applications across medical and healthcare domains, covering fundamental concepts and advanced techniques in medical image analysis. The book examines state-of-the-art deep learning methods used in real-world clinical settings, demonstrating how neural networks with multiple layers between input and output deliver significant advantages for diagnostic accuracy and patient care. Part of Springer's Intelligent Systems Reference Library series (Volume 171), this resource bridges the gap between theoretical artificial intelligence foundations and practical implementation in healthcare environments, making it essential for researchers, practitioners, and students working at the intersection of machine learning and medicine.

Key Features

Deep Learning in Healthcare covers medical image analysis fundamentals and current advances in diagnostic imaging technology.

The book presents state-of-the-art deep learning methods specifically designed for healthcare applications and clinical decision support.

Springer's Intelligent Systems Reference Library Volume 171 integrates theoretical artificial intelligence concepts with real-world healthcare implementation examples.

This resource explains how deep neural networks with multiple layers improve medical diagnosis and patient outcomes in clinical practice.

Features practical, deep learning-based clinical computer-aided diagnosis systems applicable to modern healthcare settings.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
27 November 2019
Listed Since
03 September 2019

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No barcode data available

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