Advances in Computational Intelligence for Health Informatics and Computer-Aided Diagnosis: Methods, Applications, and Tools

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.

Table of 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

About the Author :
Dr A. Malini is an associate professor in the School of Computer Science and Engineering at Vellore Institute of Technology, Chennai, Tamil Nadu. She has 20+ years of teaching experience. She obtained her doctoral degree from Anna University, Chennai. She is a lifetime member of Computer Society of India. She has published 50+ research articles in refereed journals and international/national conferences. She has contributed 20+ chapters to books. She has contributed to three edited books on artificial intelligence (AI) and machine learning. Dr. Malini has contributed voluntarily as a reviewer for reputed international journals and session chair for international conferences. She acted as a mentor and won Smart India Hackathon 2022 held at NIT Assam. She holds two Indian design patents. Her research interests include software engineering, software testing, mobile application development, green computing, Internet of Things (IoT), AI, and machine learning. Surbhi B. Khan is currently working at the School of Science, Engineering and Environment at University of Salford, United Kingdom. She is listed in the top 2% researchers released by Stanford University, USA. She earned Project Management Professional Certification from the reputed Project Management Institute, USA. She also has an Adjunct Professor position from Chandigarh University, India. She has more than 13 years of academic and teaching experience in different universities. She has published 150+ papers in many reputed journals in high indexed outlets, with many best paper awards. Her areas of interests are sentiment analysis, deep learning/machine learning, and data science in healthcare. 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 holds a Ph.D. in Compressive Sensing and Signal Processing from the SRM Institute of Science and Technology, awarded in 2021. With over 20 years of academic experience, has made significant contributions to the fields of signal processing, machine learning, and healthcare technologies. She has published 40+ papers in renowned international journals and conference proceedings, showcasing her expertise in areas such as deep learning, IoT-based healthcare systems, and AI-driven disease prediction models. She has contributed 10+ chapters to books. Mo Saraee holds a chair in data science. His research focuses on data science, machine learning, data and text mining, natural language processing (NLP), big data, and medical informatics, addressing multi-disciplinary, cross-school topics with transformative impact and benefiting local communities, with a dedication to action through the 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. His research has been published in leading journals, such as the British Medical Journal, Knowledge- Based Systems, and Neurocomputing. He is the Editor-in-Chief for the International Journal of Web Research.