This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, ...

The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and ...

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS ...

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining.

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS ...

The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation.

It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice.

This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis.

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases.