Full Description
This handbook provides thorough, in-depth, and well-focused developments of artificial intelligence (AI), machine learning (ML), deep learning (DL), natural language processing (NLP), cryptography, and blockchain approaches, along with their applications focused on healthcare systems.
Handbook of AI-Based Models in Healthcare and Medicine: Approaches, Theories, and Applications highlights different approaches, theories, and applications of intelligent systems from a practical as well as a theoretical view of the healthcare domain. It uses a medically oriented approach in its discussions of human biology, healthcare, and medicine and presents NLP-based medical reports and medicine enhancements. The handbook includes advanced models of ML and DL for the management of healthcare systems and also discusses blockchain-based healthcare management. In addition, the handbook offers use cases where AI, ML, and DL can help solve healthcare complications.
Undergraduate and postgraduate students, academicians, researchers, and industry professionals who have an interest in understanding the applications of ML/DL in the healthcare setting will want this reference on their bookshelf.
Contents
Chapter 1 Edge Computing in Healthcare: Concepts, Tools, Techniques, and Use Cases
Chapter 2 History and Role of AI in Healthcare and Medicine
Chapter 3 Drug Discovery Using Explainable AI Approaches: The Current Scenario
Chapter 4 Supervised Learning Models for Diagnosing Severity of Cirrhosis Disease
Chapter 5 3D Volumetric Computed Tomography from 2D X-Rays: A Deep Learning Perspective
Chapter 6 GAN-Based Encoder-Decoder Model for Multi-Label Diagnostic Scan Classification and Automated Radiology Report Generation
Chapter 7 A Survey of Machine Learning- and Deep Learning-Based Techniques for Diabetic Retinopathy Screening
Chapter 8 An Embedded Solution for Real-Time Implementation of a Deep Learning Model for Malicious Breast Tumour Detection
Chapter 9 Towards Robust Diagnosis of Alzheimer's Disease Using Ensemble Framework of Convolutional Neural Network and Vision Transformer
Chapter 10 RetinalAlexU-Net: Segmentation of the Retinal Vascular Network for the Diagnosis of Diabetic Retinopathy
Chapter 11 Decoding EEG Signals to Generate Images Using GANs
Chapter 12 Mental Health Disorder through Electroencephalogram Analysis using Computational Model
Chapter 13 Machine Learning Techniques in ECG Data Analysis for Medical Applications
Chapter 14 Heartcare Assistance System: A Machine Learning-Based Cardiovascular Risk Monitoring Tool (CRMT)
Chapter 15 Parameter Estimation of Real-Time NCS Signal Acquired Using Designed Neurostimulator to Develop Microcontroller-Based Healthcare Support System
Chapter 16 Critical Analysis of Current Healthcare Applications for Diagnosis of Diseases: Pitfalls and Future
Chapter 17 Machine Learning-Based Decision Support System for Optimal Treatment of Acute Inflammation Response with Specific Patient Conditions
Chapter 18 Digital Histopathology: Paving Future Directions Towards Predicting Diagnosis of Disease Via Image Analysis
Chapter 19 Artificial Intelligence Techniques to Design Epitope-Mapped Vaccines and Diagnostics for Emerging Pathogens
Chapter 20 DN-Based DTI Model to Identify Potential Drug Molecules Against COVID-19
Chapter 21 Deep Learning-Based Chatbots for Patient Queries
Chapter 22 Autism, ADHD and Dyslexia Disorder Comorbidity: An Enhanced Study on Education for Children through Artificial Intelligence-Enabled Personalized Assistive Tools
