Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches
- 1st Edition - January 1, 2026
- Latest edition
- Editors: Pawel Plawiak, Allam Jaya Prakash, Kiran Kumar Patro
- Language: English
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 med… Read more
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.
- Investigates opportunities and challenges of deep learning, including convolutional neural networks (CNNs) and their applications in medical image processing
- Includes comprehensive examination and elucidation of Kronecker convolutional procedures and their significance in medical image processing
- Explores specific medical imaging tasks where Kronecker convolutions prove beneficial
- Provides detailed examples demonstrating how convolutions may be employed to improve healthcare, offering insights into how deep learning is currently being used in clinical settings
Medical imaging researchers and biomedical engineers specializing in deep learning applications
Section 1. Foundational Concepts
1. Introduction to Deep Learning in Medical Imaging
2. Fundamentals of Convolutional Neural Networks
Section 2. Advanced Techniques in Deep Learning with Kronecker Convolutions
3. Kronecker Convolutions: A Deep Dive
4. Image Processing Techniques in Healthcare
Section 3. Applications in Medical Imaging
5. Kronecker Convolutions in Tumor Detection
6. Enhancing Organ Segmentation with Deep Learning
7. Disease Classification through Advanced Neural Networks
Section 4. Real-World Implementation
8. AI-Driven Diagnostic Imaging
9. Precision Medicine through Imaging Analytics
10. Wearable Devices and Continuous Monitoring
Section 5. Future Directions and Conclusion
11. Challenges and Future Directions in Medical Image Analysis
12. Conclusion and Future Trends
1. Introduction to Deep Learning in Medical Imaging
2. Fundamentals of Convolutional Neural Networks
Section 2. Advanced Techniques in Deep Learning with Kronecker Convolutions
3. Kronecker Convolutions: A Deep Dive
4. Image Processing Techniques in Healthcare
Section 3. Applications in Medical Imaging
5. Kronecker Convolutions in Tumor Detection
6. Enhancing Organ Segmentation with Deep Learning
7. Disease Classification through Advanced Neural Networks
Section 4. Real-World Implementation
8. AI-Driven Diagnostic Imaging
9. Precision Medicine through Imaging Analytics
10. Wearable Devices and Continuous Monitoring
Section 5. Future Directions and Conclusion
11. Challenges and Future Directions in Medical Image Analysis
12. Conclusion and Future Trends
- Edition: 1
- Latest edition
- Published: January 1, 2026
- Language: English
PP
Pawel Plawiak
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.
Affiliations and expertise
Dean of the Faculty of Computer Science and Telecommunications of the Cracow University of Technology, PolandAP
Allam Jaya Prakash
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.
Affiliations and expertise
Sr. Asst. Prof. Gr-I, School of Computer Science and Engineering, VIT Vellore University, IndiaKP
Kiran Kumar Patro
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.
Affiliations and expertise
Assistant Professor, Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, India