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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: Jaya Prakash Allam, 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

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

JA

Jaya Prakash Allam

Jaya Prakash Allam received his PhD in Electronics and Communication Engineering from the National Institute of Technology Rourkela, India, specializing in artificial intelligence. He is a Research Scientist and Postdoctoral Fellow at United Arab Emirates University, Al Ain, UAE, and has academic and research experience spanning India and the United Arab Emirates. His research focuses on biomedical signal processing, deep learning, machine learning, wearable and Edge AI systems, explainable artificial intelligence, and remote sensing. His work centers on the development of intelligent healthcare technologies, including AI-driven analysis of physiological signals and clinical decision-support systems. He serves as Associate Editor of a leading journal in biomedical and health informatics, Editor-in-Chief of Frontiers in Biomedical Signal Processing, and Academic Editor of PLOS Computational Biology. His current interests include biomedical data analytics, resource-efficient intelligent systems, and the translation of AI technologies into real-world healthcare applications.

Affiliations and expertise
United Arab Emirates University, Al Ain, United Arab Emirates

KP

Kiran Kumar Patro

Dr. Kiran Kumar Patro, Ph.D., is Associate Professor in the Department of Electronics and Communication Engineering at Aditya Institute of Technology and Management (A), Tekkali, India. He earned his Ph.D. in Electronics and Communication Engineering from Andhra University, with research focused on artificial intelligence and machine learning applications. His research interests include biomedical signal and image processing, deep learning, Edge AI, Internet of Things (IoT)-enabled intelligent systems, and federated learning. Dr. Patro serves as an Academic Editor for PLOS ONE and is a member of the editorial boards of BMC Artificial Intelligence and Frontiers in Bioinformatics. His work focuses on the development and evaluation of intelligent computational approaches for healthcare and engineering applications, with particular emphasis on AI-driven signal processing and distributed intelligent systems. In this volume, he contributes expertise in benchmarking methodologies, Edge AI systems, and federated learning frameworks.
Affiliations and expertise
Aditya Institute of Technology and Management (A), Tekkali, Andhra Pradesh, India

PP

Pawel Plawiak

Dr. Paweł Pławiak is Professor and Dean of the Faculty of Computer Science and Telecommunications at Cracow University of Technology, Poland. He holds a Ph.D. in Biocybernetics and Biomedical Engineering from AGH University in Kraków and a D.Sc. in Technical Computer Science and Telecommunications from the Silesian University of Technology. His research focuses on machine learning, computational intelligence, signal processing, and biomedical engineering, with particular interests in neural networks, evolutionary computation, ensemble learning, and deep learning methods. His work has contributed to the application of advanced computational techniques for the analysis and interpretation of complex biomedical and engineering data. In this volume, he provides expertise in machine learning methodologies and supports the development of rigorous benchmarking and evaluation approaches across the covered topics.

Affiliations and expertise
Cracow University of Technology, Poland

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