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.- 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
Table of Contents
Section 1: Foundational concepts1 Introduction to deep learning in medical imaging2 Fundamentals of convolutional neural networksSection 2: Advanced techniques in deep learning with kronecker convolutions3 Kronecker convolutions ensemble vision transformer and 3D kronecker U-net for volumetric segmentation of kidney stones, cysts and tumor from CT scans4 Image processing techniques in healthcare for early detection of heart diseasesSection 3: Applications in medical imaging5 Automated atypical teratoid /rhabdoid tumor detection in magnetic resonance imaging using deep learning6 Ischemic stroke lesion segmentation using multiscale processing and knowledge distillation through intra-domain teacher7 Disease classification through advanced neural networksSection 4: Real-world implementation8 GAT-Net: ghost attention network for classification of gait-based neurodegenerative diseases9 Artificial intelligence-enhanced diagnostics: deep learning in medical imaging10 Precision medicine through imaging analytics: Kronecker convolutions in tumor detection11 Diagnosis of schizophrenia using convolutional neural networks based on multichannel electroencephalography signal12 Detection of anomalies in physiological signals using artificial neural network13 Advancements in electrocardiography-based detection of obstructive sleep apnea: a deep learning approach14 Machine learning-based life expectancy post chest surgerySection 5: Future directions and conclusion15 Challenges and future directions in medical image analysis
