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

  • 1st Edition - January 27, 2026
  • Latest edition
  • Editors: Allam Jaya Prakash, Kiran Kumar Patro, Pawel Plawiak
  • 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

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Description

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.

Key features

  • 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

Readership

Medical imaging researchers and biomedical engineers specializing in deep learning applications

Table of contents

Section 1: Foundational concepts

1 Introduction to deep learning in medical imaging
  • Sakshi Gupta, Anwesha Sengupta
2 Fundamentals of convolutional neural networks
  • Shubhobrata Bhattacharya, Anirban Dasgupta, Anwesha Sengupta, Khushi Dutta

Section 2: Advanced techniques in deep learning with kronecker convolutions

3 Kronecker convolutions ensemble vision transformer and 3D kronecker U-net for volumetric segmentation of kidney stones, cysts and tumor from CT scans
  • Santoshi Gorli, Ratnakar Dash
4 Image processing techniques in healthcare for early detection of heart diseases
  • Shaik Salma Asiya Begum, Ruqsar Zaitoon

Section 3: Applications in medical imaging

5 Automated atypical teratoid /rhabdoid tumor detection in magnetic resonance imaging using deep learning
  • D. Santhadevi, Prajwal Sri Tej Aitty, A.V.S. Hemanth Kumar, T.K. Vamshi Krishna
6 Ischemic stroke lesion segmentation using multiscale processing and knowledge distillation through intra-domain teacher
  • Chintha Sri Pothu Raju, Rabul Hussain Laskar
7 Disease classification through advanced neural networks
  • Anirban Dasgupta, Shubhobrata Bhattacharya, Anwesha Sengupta, Aman Paul

Section 4: Real-world implementation

8 GAT-Net: ghost attention network for classification of gait-based neurodegenerative diseases
  • Mohammad Iman Junaid, Arghyadip Bagchi, Samit Ari
9 Artificial intelligence-enhanced diagnostics: deep learning in medical imaging
  • Harmanpreet Kaur, Gurwinder Singh
10 Precision medicine through imaging analytics: Kronecker convolutions in tumor detection
  • Sesikala Bapatla, Spandana Mande
11 Diagnosis of schizophrenia using convolutional neural networks based on multichannel electroencephalography signal
  • Sylwia Zemła, Hubert Orlicki, Mateusz Fudala, Julia Polak, Arkadiusz Knapik, Wojciech Książek
12 Detection of anomalies in physiological signals using artificial neural network
  • K. Jayashree, Ganesh V. Bhat, Shivashankar Hiremath, M.H. Shrishail
13 Advancements in electrocardiography-based detection of obstructive sleep apnea: a deep learning approach
  • Venkata Phanikrishna Balam, SujayKumar Reddy M.
14 Machine learning-based life expectancy post chest surgery
  • G. Gopichand, Harshith Avineni, Harshavardhan Kothapalle, Gowtham Cherukuri, Varshith G, Sasith Kotluri

Section 5: Future directions and conclusion

15 Challenges and future directions in medical image analysis
  • Hamidreza Ashayeri, Navid Sobhi, Hadi Vahedi, Roohallah Alizadehsani, Ali Jafarizadeh

Product details

  • Edition: 1
  • Latest edition
  • Published: January 27, 2026
  • Language: English

About the editors

AP

Allam Jaya Prakash

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 @: [email protected], [email protected].

Affiliations and expertise
Department of Electrical and Communication Engineering, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates

KP

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
Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Tekkali, Srikakulam, India

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
Faculty of Computer Science and Mathematics of the Cracow University of Technology, Warszawska, Krakow, Poland

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