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

The proposed 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 healthcare 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. •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.
The book is for students, scholars and professionals of biomedical technology and healthcare data analytics.