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

Allam Jaya Prakash

ISBN 10: 0443330824 / ISBN 13: 9780443330827

Edité par Elsevier Science Publishing Co Inc Jan 2026, 2026

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780443330827

Détails bibliographiques

• Titre: Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches
• Éditeur: Elsevier Science Publishing Co Inc Jan 2026
• Date de parution: 2026
• Langue: anglais
• ISBN à 10 chiffres: 0443330824
• ISBN à 13 chiffres: 9780443330827
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 276 L x 216 H x 16 P (en millimètres) 276 millimètres de largeur par 216 millimètres de hauteur par 16 millimètres de profondeur
• Poids: 450 grammes

A propos de ce titre

Synopsis

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.

À propos des auteurs

Pawel Plawiak was born in Ostrowiec, Poland, in 1984. He holds BEng and MSc degrees in electronics and telecommunications, a PhD (honors) in biocybernetics and biomedical engineering from the AGH University of Science and Technology, Cracow, Poland, and a DSc degree in technical computer science and telecommunications from Silesian Technical University, Gliwice, Poland. He is the Head of Department of Information and Communications Technology and an Associate Professor in Cracow University of Technology, Krakow, and Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice. He has published more than 70 papers in refereed international SCI-IF journals. His research interests include machine learning and computational intelligence (e.g., artificial neural networks, genetic algorithms, fuzzy systems, support vector machines, k-nearest neighbours, and hybrid systems), ensemble learning, deep learning, evolutionary computation, classification, pattern recognition, signal processing and analysis, data analysis and data mining, sensor techniques, medicine, biocybernetics, biomedical engineering and telecommunications.

Allam Jaya Prakash was born in Venkampeta (Village), Parvatipuram (District), Andhra Pradesh, India, in 1988. He received a BTech degree in electronics and communication engineering from GITAS, Piridi, affiliated to Jawaharlal Nehru Technological University Kakinada (JNTUK), India, in 2009, and an MTech degree in digital electronics and communication systems from the GMRIT, Rajam, affiliated to Jawaharlal Nehru Technological University Kakinada (JN TUK), in 2012. He completed his Ph.D. (Artificial Intelligence) from NIT Rourkela, Rourkela, Odisha, India. Presently he is working as Senior Assistant Professor in the School of Computing Science and Engineering (SCOPE), VIT Vellore, India. His current area of research includes biomedical signal processing, machine learning, and deep learning techniques.

Kiran Kumar Patro holds ME and PhD degrees from the Department of Electronics and Communication Engineering, Andhra University, Visakhapatnam, India. He first worked as a UGC junior research fellow (Govt. of India) for 2 years and then as a senior research fellow for 3 years at Andhra University. His research interests include biomedical signal processing, image processing, pattern recognition and machine learning. He currently works as an Assistant professor in the Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management. He has published more than 24 papers in refereed international journals. He is an active peer reviewer for reputed journals of IEEE, Elsevier, Springer, Wiley, etc.