Name: Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches
SKU: 65760101
Price: 162.42 USD
Availability: InStock
Author: Allam Jaya, BTech, MTech, PhD Prakash;Pawel, BEng, MSc, PhD, DSc Plawiak;Kiran Kumar, ME, PhD Patro
ISBN: 9780443330827

portada Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches

Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches

Formato

Libro Físico

Editorial

Academic Press Inc

Autor

Allam Jaya, BTech, MTech, PhD Prakash;Pawel, BEng, MSc, PhD, DSc Plawiak;Kiran Kumar, ME, PhD Patro

Año

2026

N° páginas

300

Encuadernación

Tapa Blanda

Dimensiones

23.5x19.1 cm

ISBN13

9780443330827

Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches

Allam Jaya, Btech, Mtech, Phd Prakash;Pawel, Beng, Msc, Phd, Dsc Plawiak;Kiran Kumar, Me, Phd Patro (Autor) · Academic Press Inc · Tapa Blanda

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Reseña del libro "Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches"

Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches focuses on the use of deep learning techniques in the field of medical imagine analysis. These advances offer promising progress in healthcare through improvements in diagnostic accuracy, efficiency in medical image interpretation, and breakthroughs in treatment planning. Divided into five sections, the book begins with foundational coverage of deep learning in medical imaging and fundamentals of Convolutional Neural Networks. Discover the role convolutions play in extracting meaningful features from images, aiding tasks such as diagnosis and segmentation. The second section takes a deep dive into Kronecker convolutions and their unique advantages, such as enhanced spatial hierarchy understanding, efficient parameter utilization, and improved adaptability to specific characteristics of medical images. Section three reviews specific applications in tumor detection, enhancing organ segmentation as well as disease classification, and section four explores real-world implementation of AI-driven diagnostic imaging, precision medicine via imaging analytics, and wearable devices and continuous health monitoring. The final section offers discussion on the unique challenges, trends, and potential future directions these innovative computational approaches have on medical image processing and advanced healthcare. In summary, this book takes an interdisciplinary approach to bridge the gap between theory and practice, fusing knowledge from the domains of medicine, computer science, and machine learning to address issues in healthcare through sophisticated image analysis techniques.