Meta-learning, or learning to learn, has been gaining popularity in recent years to adapt to new tasks systematically and efficiently in machine learning. In the book, Meta-Learning Frameworks for Imaging Applications, experts from the fields of machine learning and imaging come together to explore the current state of meta-learning and its application to medical imaging and health informatics. The book presents an overview of the meta-learning framework, including common versions such as model-agnostic learning, memory augmentation, prototype networks, and learning to optimize. It also discusses how meta-learning can be applied to address fundamental limitations of deep neural networks, such as high data demand, computationally expensive training, and limited ability for task transfer.
One critical topic in imaging is image segmentation, and the book explores how a meta-learning-based framework can help identify the best image segmentation algorithm, which would be particularly beneficial in the healthcare domain. This book is relevant to healthcare institutes, e-commerce companies, and educational institutions, as well as professionals and practitioners in the intelligent system, computational data science, network applications, and biomedical applications fields. It is also useful for domain developers and project managers from diagnostic and pharmacy companies involved in the development of medical expert systems. Additionally, graduate and master students in intelligent systems, big data management, computational intelligent approaches, computer vision, and biomedical science can use this book for their final projects and specific courses.

1. Optimization Approaches in Meta-Learning Models 1

2. A Review on Image Super-Resolution Using GAN 12

3. Optimizing Hyper Meta Learning Models: An Epic 32

4. A Study on Evaluation and Analysis of Edge Detection Operators 65

5. Augmented Reality and Its Significance in Healthcare Systems 103

6. Automated Diagnosis of Eye Problems Using Deep Learning Techniques on Retinal Fundus Images 119

7. Comparative Analysis and Automated Eight-Level Skin Cancer Staging Diagnosis in Dermoscopic Images Using Deep Learning 134

8. Critical Review Analysis on Deep Learning-Based Segmentation Techniques for Water-Body Extraction 153

9. Hybrid Approaches for Plant Disease Recognition: A Comprehensive Review 169

10. Redefining Management With Advent of Artificial Intelligence in the Current Business World 186

Compilation of References 198

Related References 222

About the Contributors 247

Index 252

Parveen Singh is an Associate Professor and Head Department of Computer Sciences,Govt SPMR College of Commerce, Jammu, honoured with National Award by Govt of India, for popularization of Science among children’s, is a dynamic instructor and thought leader, focused on providing students with a rigorous and challenging education. Earned recognition as a knowledgeable teacher, author along with as a debater with well-organized, stimulating, and student-centred courses. Cultivated teaching partnerships and alliances with key business contacts across the J&k . Co-authored 09 Books and attended more than 17 international and national conferences. Certified, Research Based Pedagogical tools Trainer from Centre Of Excellence in Science and Mathematics Education IISER pune. Awarded with Govt. of J&K, Science Innovative Teacher Award 2013, Awarded with Govt of Tamil Nadu ICTACT Best Techno Faculty Award 2016, Awarded with Principal Citation for contribution towards Growth of Institution 2010,Awarded with Distinguished HOD award 2017, by Computer Society of India, CSI, Mumbai Chapter, Member of Jammu University Academic council from 2006-2009,Member of Board of Studies, Jammu University since 2003.

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