Full Description
This book provides a comprehensive overview of the intersection of computational intelligence, health informatics, and computer-aided diagnosis (CAD). The book explores and highlights the latest advancements, methodologies, applications, and tools in these fields.
Advances in Computational Intelligence for Health Informatics and Computer-Aided Diagnosis: Methods, Applications, and Tools covers a broad spectrum of computational intelligence approaches, from basic concepts to advanced methodologies. The focus on health informatics reflects the book's commitment to researching data integration, privacy issues, and interoperability issues that are crucial in today's healthcare landscape. The book's core is its in-depth examination of CAD systems, which encompasses numerous healthcare sectors and underlines the technological complexity involved in building accurate and efficient diagnostic tools. Some of the other key areas covered include: medical imaging analysis, disease identification and diagnosis, and drug research and development. It also provides case studies that demonstrate how computational intelligence methods are applied in real-world healthcare scenarios, giving readers a practical understanding of the subject matter. The authors then discuss future trends and directions in computational intelligence for health informatics.
The book is designed to serve as a guide to for academics, professionals, and students who are curious about the challenges of integrating contemporary computational approaches into medical diagnostics and decision support.
Contents
Chapter1- Overview of Computational Intelligence for Health Informatics and Computer-Aided Diagnosis
Chapter 2- From Pixels to Prognosis: Machine Learning Approaches for Medical Imaging Diagnosis
Chapter 3- Development of an Advanced Lung Cancer Diagnosis System Using Image Processing and Machine Learning
Chapter 4- Automated Dementia Detection using Genetic Algorithm and Differential Evaluation Model P. Muthu
Chapter 5- Exploring deep learning models in medical image analysis for human disease detection and classification
Chapter 6- Machine Learning approach for different habitual activity Versus sleep intermittent stages in time efficient perspectives based on facial features
Chapter 7- A Survey on Challenges in Interoperability and Security in iot based healthcare system
Chapter 8- Intelligent Cardiovascular Disease Prediction Using Ant Colony Optimization with Enhanced Deep Learning Model
Chapter 9- Utilizing Explainable Artificial Intelligence for Parkinson's Disease Diagnosis through Analysis of Spiral and Wave Drawings with Integrated Data Augmentation
Chapter 10- Review on Medical sensors for health care monitoring systems using Machine learning algorithm
Chapter 11- Breast Cancer Classification Using Machine Learning - a Study
Chapter 12- Secure Compressive Sensing in Medical Imaging Using Fractional Order Hyper Chaotic Systems
Chapter 13- Early-Stage Lung Cancer Classification through Improved Data Processing with Spatial FusionNet
Chapter 14- Practical Applications: Specific Diseases or Conditions Where AI has made a Significant Impact: A review
Chapter 15- Medical Impact Assessment of Industrial Emissions: Predicting Air Quality Index
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