Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare.

Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.

Topics Covered
The many academic areas covered in this publication include, but are not limited to:
•Artificial Intelligence
•Artificial Neural Networks
•Computer-Aided Diagnosis
•Critical Care
•Disease Detection Techniques
•Disease Prediction
•Healthcare Analysis
•Image Analysis
•Machine Learning
•Medical Data