Next-Gen Healthcare

Chapter 1  Adversarial Threats in Healthcare: A Comprehensive Analysis of Vulnerabilities, Defense Mechanisms, and Recent Research.- Chapter 2 Masked Autoencoder-Based Domain Adaptation for Cross-Population Breast-Lesion Classification in Mammograms.- Chapter 3 Optimized Block-Wise Fine-Tuning of VGG Models for Accurate and Explainable Detection of Chest Infectious Diseases Using Chest X Rays.- Chapter 4 Quantum Neural Network for Robust Image Classification: Applications to Medical and Benchmark Datasets.-  Chapter 5 Explainable Machine Learning Approaches for Cardiovascular Disease Detection: A Comparative Study on the UCI Heart Disease Dataset.- Chapter 6 Large Language Models (LLMs) in Medical Error Detection and Correction: A Comprehensive Review.- Chapter 7 Data Privacy and Security in Large Language Models for Medical Fields.- Chapter 8 Advancing Early Alzheimer's Diagnosis with Deep Learning on MRI Data.- Chapter  9 Predictive Models for Early Detection and Prognosis of Dementia using Artificial Intelligence and Machine Learning.- Chapter 10 Predicting Drug Response in Diffuse Large B-Cell Lymphoma Patients Using Machine Learning Models.- Chapter 11 Guideline-Concordant Two-Stage AI for Diabetes Severity Stratification and Pharmacotherapy Recommendation.- Chapter 12 MOBPITL: Enhancing Diabetic Retinopathy Detection via PiTMobileNetV2 Fusion and Lamb Optimization.- Chapter 13 A Comprehensive Approach to Skin Lesion Classification using Machine and Deep Learning.- Chapter 14 Next-Gen Diagnostics: Utilizing AI Classification Algorithms for Enhanced Obesity Detection and Intervention.- Chapter 15 Using Explainable AI for Assessment of Depression: A Systematic Literature Review.

• Erscheinungsdatum: 20.01.2026
• Autor/Autorin: Nour Eldeen M. Khalifa
• Reihe: Medicine (R0)
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 49.7 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 464 Seiten
• ISBN: 9783032072672
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.