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

Über die Autorinnen und Autoren

Allam Jaya Prakash received the B.Tech. degree in Electronics and Communication Engineering from JNTU Kakinada, India, in 2009, the M.Tech. degree in Digital Electronics and Communication Systems from GMRIT, JNTU Kakinada, India, in 2012, and a PhD degree in Electronics and Communication Engineering from the National Institute of Technology, Rourkela, India, in 2024. He is currently a Postdoctoral Fellow in the Department of Electrical and Communication Engineering at United Arab Emirates University, Al Ain, UAE, and also serves as a Senior Assistant Professor (Grade I) in the School of Computer Science and Engineering at VIT Vellore, India. He has authored more than 30 journal and conference papers in reputable venues, including the IEEE Transactions on Artificial Intelligence, the IEEE Journal of Biomedical and Health Informatics, and Engineering Applications of Artificial Intelligence. His research interests include biomedical signal processing, deep learning, machine learning, edge AI, and remote sensing. He has also served as Guest Editor for a special issue of the IEEE Journal of Biomedical and Health Informatics. He is a regular reviewer for several international journals, including IEEE JBHI, IEEE TIM, IEEE Sensors Journal, IEEE Access, and Biomedical Signal Processing and Control. He was listed among Stanford’s Top 2% Scientists in 2024. He can be reached @: allamjayaprakash@gmail.com, allamjayaprakash@uaeu.ac.ae.

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.

Paweł Pławiak was born in Ostrowiec, Poland, in 1984. He holds B.Eng. and M.Sc. degrees in Electronics and Telecommunications in 2012, a Ph.D. (with honors) in Biocybernetics and Biomedical Engineering in 2016 from the AGH University of Science and Technology, Krakow, Poland, and a D.Sc. degree in Technical Computer Science and Telecommunications in 2020 from the Silesian University of Technology, Gliwice, Poland. He is the Dean of the Faculty of Computer Science and Mathematics and an Associate Professor at the Cracow University of Technology, Krakow, Poland. He has also served as an Associate Professor at the Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland, and as the Deputy Director for Scientific Affairs at the National Institute of Telecommunications, Warsaw, Poland. He has published more than 100 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 technologies, medicine, biocybernetics, biomedical engineering, and telecommunications.

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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.