Reprint

Artificial Intelligence Advances for Medical Computer-Aided Diagnosis

Edited by
July 2024
222 pages
  • ISBN978-3-7258-1644-6 (Hardback)
  • ISBN978-3-7258-1643-9 (PDF)

This book is a reprint of the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis that was published in

Medicine & Pharmacology
Public Health & Healthcare
Summary

This Special Issue, "Artificial Intelligence Advances for Medical Computer-Aided Diagnosis," explores the transformative role of AI and ML in modern diagnostics. Featuring contributions from leading researchers, this collection highlights innovative algorithms, models, and applications that enhance diagnostic accuracy, speed, and predictive power across various medical fields, including radiology, pathology, genomics, and personalized medicine. This issue includes 12 articles, spanning theoretical advancements, algorithm development, and practical implementations, showcasing the broad impact and future potential of AI-driven diagnostics. Each contribution offers unique insights into overcoming current challenges, improving clinical decision-making, and ultimately enhancing patient outcomes. This compilation serves as a valuable resource for researchers, clinicians, and technologists, fostering further innovation and collaboration in advancing medical diagnostics. Join us in exploring the latest breakthroughs and future directions in AI-driven healthcare, as we strive to improve diagnostic accuracy and efficiency for better patient care.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
n/a; magnetic resonance imaging (MRI); medical image reconstruction; deep learning; conditional generative adversarial networks (CGANs); parallel imaging; hybrid spatial and k-space loss function; disease severity; deep learning; machine learning; Parkinson’s disease; diabetic retinopathy; Alzheimer’s disease; CNN; MSC; artificial intelligence; deep learning; image classification; monkeypox disease; knee OA detection; DenseNet169; early stopping; self-adaptive; GCE; medical image; breast cancer diagnoses; machine learning; deep learning; classification; Parkinson’s disease (PD); filter feature selection; ensemble learning; genetic selection; machine learning; decision support system; medical diagnosis; influenza; artificial intelligence; stuttering detection; systematic review; rehabilitation; machine learning; ChatGPT; KARA-CXR; chest X-ray; LLM; cardiovascular diseases; deep learning; disease detection; heart diseases; machine learning; ensemble learning; XGBoost; bank of features; coronary angiograms; evolutionary algorithm; feature selection; K-nearest neighbor; stenosis classification; n/a