Handbook of Deep Learning in Biomedical Engineering: Techniques and Applications

Medical images and clinical signals pose computational challenges that deep-learning methods are increasingly used to investigate. Handbook of Deep Learning in Biomedical Engineering: Techniques and Applications brings together research chapters on these methods, with coverage ranging from neural-network approaches to medical image processing and disease detection.

Edited by Valentina Emilia Balas, Brojo Kishore Mishra, and Raghvendra Kumar, this scholarly reference offers a broad, chapter-by-chapter view of deep learning across biomedical engineering—and includes some applications beyond healthcare, too.

Deep learning across biomedical applications

The collection introduces readers to deep-learning techniques and their use in areas such as biomedical image analysis, medical signals, and computer-aided diagnosis. Chapters discuss convolutional neural networks, image processing, and tools and algorithms used in deep-learning work.

From medical images to disease detection

Several contributions focus on how computational models can be applied to medical imagery and disease-related problems. Topics include CNN architectures for medical imaging, image segmentation and interpretation, early disease recognition, and depression detection in cancer communities. The range of examples makes this a useful reference for readers comparing approaches across distinct biomedical tasks.

A wide-ranging collection of research topics

The book’s scope is not limited to clinical settings. Its chapters also consider blockchain technology in education and deep-learning methods for plant disease recognition and classification. These varied subjects show how the volume approaches deep learning as a set of methods applied across different domains, rather than as a single-purpose medical imaging manual.

For postgraduate study and research

This reference is aimed at readers working or studying in computer science, biomedical engineering, and related research areas. Graduate and PhD students, lecturers, scientific researchers, and clinicians interested in computational methods may find its collected applications and research discussions relevant to their work.

For a focused look at the meeting point of deep learning and biomedical engineering, this volume brings together practical application areas, technical approaches, and research questions in one edited collection.