Deep Learning in Medical Image Analysis (Artificial Intelligence in Smart Healthcare Systems)

✍ Scribed by R. Indrakumari (editor), T. Ganesh Kumar (editor), D. Murugan (editor), Sherimon P.C. (editor)

• Publisher: Chapman and Hall/CRC
• Year: 2024
• Tongue: English
• Leaves: 197
• Edition: 1
• Category: Library

✦ Synopsis

This book is designed as a reference text and provides a comprehensive overview of conceptual and practical knowledge about deep learning in medical image processing techniques. The post-pandemic situation teaches us the importance of doctors, medical analysis, and diagnosis of diseases in a rapid manner. This book provides a snapshot of the state of current research between deep learning, medical image processing, and health care with special emphasis on saving human life. The chapters cover a range of advanced technologies related to patient health monitoring, predicting diseases from genomic data, detecting artefactual events in vital signs monitoring data, and managing chronic diseases. This book

• Delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field
• Presents key principles by implementing algorithms from scratch and using simple MATLAB®/Octave scripts with image data
• Provides an overview of the physics of medical image processing alongside discussing image formats and data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, and tomographic reconstruction
• Highlights the new potential applications of machine learning techniques to the solution of important problems in biomedical image applications

This book is for students, scholars, and professionals of biomedical technology and healthcare data analytics.

✦ Table of Contents

Cover
Half Title
Series
Title
Copyright
Contents
Editor Biographies
List of Contributors
Chapter 1 Journey into the Digital Frontier: Demystifying Neural Networks and Deep Learning
Chapter 2 An In-Depth Analysis of Deep Learning’s Multifaceted Influence on Healthcare Systems
Chapter 3 Monitoring and Diagnosis of Health Using Deep Learning Methods
Chapter 4 A Survey: Recent Advances and Clinical Applications of Deep Learning in Medical Image Analysis
Chapter 5 A Deep Learning Framework to Detect Diabetic Retinopathy Using CNN
Chapter 6 Skin Cancer Detection and Classification Using Deep Learning Techniques
Chapter 7 Prediction of Epidermis Disease Outbreak Using Deep Learning
Chapter 8 Deep Learning-Based Medical Image Segmentation: A Comprehensive Investigation
Chapter 9 Unleashing the Potential of Deep Learning in Diabetic Retinopathy: A Comprehensive Survey
Chapter 10 Enhancing Cardiovascular Health Diagnosis through Predictive Analysis of Electronic Health Records
Index