图书简介
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.
Chapter 1 Edge Computing in Healthcare: Concepts, Tools, Techniques, and Use Cases
Shalini Ramanathan, Anabel Pineda-Briseno, Tauheed Khan Mohd, and Mohan Ramasundaram
Chapter 2 History and Role of AI in Healthcare and Medicine
Dipali Dakhole and K.N. Praveena
Chapter 3 Drug Discovery Using Explainable AI Approaches: The Current Scenario
Chinju John, Akarsh K. Nair, and Jayakrushna Sahoo
Chapter 4 Supervised Learning Models for Diagnosing Severity of Cirrhosis Disease
Akshita Sakshi, J.V. Bibal Benifa, and P. Antony Seba
Chapter 5 3D Volumetric Computed Tomography from 2D X-Rays: A Deep Learning Perspective
Manish Kumar, Suman Kumar Maji, and Hussein Yahia
Chapter 6 GAN-Based Encoder-Decoder Model for Multi-Label Diagnostic Scan Classification and Automated Radiology Report Generation
Rahul Kumar, K. Karthik, and S. Sowmya Kamath
Chapter 7 A Survey of Machine Learning- and Deep Learning-Based Techniques for Diabetic Retinopathy Screening
Nitigya Sambyal, Poonam Saini, and Rupali Syal
¿Chapter 8 An Embedded Solution for Real-Time Implementation of a Deep Learning Model for Malicious Breast Tumour Detection
S. Malarvizhi, R. Kayalvizhi, H. Heartlin Maria, Revathi Venkatraman, Shatanu Patil, and A. Maria Jossy
Chapter 9 Towards Robust Diagnosis of Alzheimer’s Disease Using Ensemble Framework of Convolutional Neural Network and Vision Transformer
Poonguzhali Elangovan and Malaya Kumar Nath
Chapter 10 RetinalAlexU-Net: Segmentation of the Retinal Vascular Network for the Diagnosis of Diabetic Retinopathy
A. Sathya Vani and D. Sumathi
Chapter 11 Decoding EEG Signals to Generate Images Using GANs
Ritik Naik, Kunal Chaudhari, Ketaki Jadhav, and Amit Joshi
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