Deep Learning in Medical Image Analysis: Recent Advances and Future Trends (Artificial Intelligence in Smart Healthcare Systems)

"Deep Learning in Medical Image Analysis: Recent Advances and Future Trends" book is an all-embracing work that provides a great overview of the conceptual aspects and the state of the art as well as key problems and possible solutions in the field of medical image processing: Recent Advances and Future Trends. Thus, in the context of the post-pandemic world, where precise medical analysis is vital, this book is a reference point for identifying the main contact points between deep learning, medical imaging, and healthcare. As a direct result of its emphasis on the sanctity of human life, its chapters explain advanced techniques on patient health monitoring, disease prognosis from genomics data, identification of anomalies in the patient’s vital signs, and chronic disease management.

Designed as a foundational text, this book, "Deep Learning in Medical Image Analysis: Recent Advances and Future Trends,” offers a smooth and easy guide to imaging in the medical field and its key requirements. Applying simple MATLAB®/Octave scripts in concert with the image data effectively explains solutions and procedures in relation to key principles. In addition, it enters into the fundamentals of medical image physics, including image format, medical image storage, basic image intensity transform approach, medical image filter, medical Fourier transform, spatial transform, volume rendering technique, medical image registration, medical tomographic reconstruction, etc.

This book, "Deep Learning in Medical Image Analysis: Recent Advances and Future Trends,” despite its theoretical approach, pays significant attention to the implementation, demonstrating the ability of machine learning to enhance biomedical imaging applications. In this way, it advances existing theory by explaining how concepts can be learned with practical exercises for applying machine learning to important problems in healthcare.

Deep Learning in Medical Image Analysis: Recent Advances and Future Trends Table of Contents:

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

Who is this course for?

1. For students, scholars, and professionals of biomedical technology and healthcare data analytics.

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