Machine Learning for Medical Applications

Computer Vision, Image Processing, Disease Detection

Ranjith Rajamanickam, Amit Sharma, Dhivya Ranjith, J. Paulo Davim

• Publisher: De Gruyter
• Year: 2025
• eBook ISBN: 9783112205297
• Topic: Technology & Engineering
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Technology & Engineering

Machine Learning for Medical Applications – Volume II delves into the intersection of artificial intelligence, computer vision, and healthcare, offering a comprehensive exploration of how machine learning is revolutionizing disease detection and diagnostics. With a focus on deep learning methods, the volume covers a wide spectrum of innovations including medical image segmentation, predictive modeling, tissue engineering, smart biomaterials, and personalized implant design through 3D printing. Contributors from academia and industry present state-of-the-art applications involving quantum dot functionalization, AI-enhanced diagnostic materials, and real-time image analysis. Each chapter provides both foundational knowledge and practical insight into how advanced algorithms can drive medical breakthroughs. Ideal for medical technologists, data scientists, biomedical engineers, and clinical practitioners, this volume emphasizes the role of machine learning in developing faster, smarter, and more accurate diagnostic tools for the next generation of personalized medicine.

Table of contents

• Title Page
• Copyright
• Contents
• Frontmatter
• Contents
• List of contributors
• Deep learning in computer vision
• Deep learning for medical image segmentation
• Deep learning for image segmentation
• Machine learning algorithm for medical image processing
• Machine learning models for predicting anomaly in scanned images
• Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data
• AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics
• Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging
• Machine learning application in tissue engineering: scaffold design
• Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications
• Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds
• Customization of medical implants using 3D printing
• Index
• Index