Advances in Computational Intelligence for Health Informatics and Computer-Aided Diagnosis
CRC Press (Verlag)
978-1-032-77657-6 (ISBN)
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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 in the challenges of integrating contemporary computational approaches into medical diagnostics and decision support.
Dr. A. Malini is an Associate Professor at Vellore Institute of Technology, Chennai, Tamil Nadu. She has 20+ years of teaching experience, and obtained her doctoral degree from Anna University, Chennai. She is a Lifetime member of Computer Society of India. She has been a mentor and won smart India Hackathon 2022 held at NIT Assam. Her research interest includes software Engineering, Software Testing, Mobile Application development, Green Computing, Internet of Things, Artificial Intelligence and Machine Learning. Dr. Surbhi B. Khan is currently working in the School of Science, Engineering and Environment at University of Salford, United Kingdom. She has earned Project Management Professional Certification from the Project Management Institute, USA. She also enjoys an adjunct professor position from Chandigarh University, India and has more than 13 years of academic and teaching experience. Her area of interests is Sentiment Analysis, Deep Learning/Machine learning, and Data science in healthcare. Dr. S. Kayalvizhi is an accomplished academician and researcher, currently serving as an Assistant Professor (Senior Grade) in the Department of Electronics and Communication Engineering at SRM University. She earned a Ph.D. in Compressive Sensing and Signal Processing from the SRM Institute of Science and Technology in 2021. With over 20 years of academic experience, she has made significant contributions to the fields of signal processing, machine learning, and healthcare technologies. Her areas of expertise include deep learning, IoT-based healthcare systems, and AI-driven disease prediction models. Professor Mo Saraee holds a chair in Data Science, the head of Computer Science and Software Engineering and is the programme leader of the MSc Data Science and MSc IoT (Internet of Things) Programmes at the University of Salford, Manchester, UK, where he also received his Ph.D. His research focuses on Data Science, Machine Learning, Data & Text Mining, NLP, Big Data, and Medical Informatics, addressing multi-disciplinary, cross-school topics with transformative impact, benefiting local communities, dedication to action through real-world application of research including developing and integrating innovative data mining approaches to improve human health, in collaboration with both Salford City Council and the NHS.
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
| Erscheint lt. Verlag | 23.4.2025 |
|---|---|
| Zusatzinfo | 47 Tables, black and white; 77 Line drawings, color; 10 Line drawings, black and white; 25 Halftones, color; 33 Halftones, black and white; 102 Illustrations, color; 43 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Technik ► Medizintechnik | |
| ISBN-10 | 1-032-77657-9 / 1032776579 |
| ISBN-13 | 978-1-032-77657-6 / 9781032776576 |
| Zustand | Neuware |
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