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Neutrosophic Set in Medical Image Analysis gives an understanding of the concepts of NS, along with knowledge on how to gather, interpret, analyze and handle medical images using NS methods. It presents the latest cutting-edge research that gives insight into neutrosophic set’s novel techniques, strategies and challenges, showing how it can be used in biomedical diagnoses systems. The neutrosophic set (NS), which is a generalization of fuzzy set, offers the prospect of overcoming the restrictions of fuzzy-based approaches to medical image analysis. Introduces the mathematical model and concepts of neutrosophic theory and methods Highlights the different techniques of neutrosophic theory, focusing on applying the neutrosophic set in image analysis to support computer- aided diagnosis (CAD) systems, including approaches from soft computing and machine learning Shows how NS techniques can be applied to medical image denoising, segmentation and classification Provides challenges and future directions in neutrosophic set based medical image analysis

Diagnostic imaging --- Mathematics. --- Fuzzy sets. --- Neutrosophic logic. --- Image Processing, Computer-Assisted --- Fuzzy Logic. --- Models, Theoretical. --- methods. --- Experimental Model --- Experimental Models --- Mathematical Model --- Model, Experimental --- Models (Theoretical) --- Models, Experimental --- Models, Theoretic --- Theoretical Study --- Mathematical Models --- Model (Theoretical) --- Model, Mathematical --- Model, Theoretical --- Models, Mathematical --- Studies, Theoretical --- Study, Theoretical --- Theoretical Model --- Theoretical Models --- Theoretical Studies --- Computer Simulation --- Systems Theory --- Logic, Fuzzy --- Smarandache logic --- Fuzzy logic --- Sets, Fuzzy --- Fuzzy mathematics --- Set theory --- Clinical imaging --- Imaging, Diagnostic --- Medical diagnostic imaging --- Medical imaging --- Noninvasive medical imaging --- Diagnosis, Noninvasive --- Imaging systems in medicine

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Neutrosophic logic and set are gaining significant attention in solving many real-life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistency, and indeterminacy. A number of new neutrosophic theories have been proposed and have been applied in Multi-Criteria Decision-Making, computational intelligence, multiple-attribute decision-making, image processing, medical diagnosis, fault diagnosis, optimization design, and so on. Neutrosophic logic, set, probability, statistics, etc., are, respectively, generalizations of fuzzy and intuitionistic fuzzy logic and set, classical and imprecise probability, classical statistics and so on. This Special Issue gathers 11 original research papers that report on the state of the art and recent advancements in Multi-Criteria Decision-Making using neutrosophic environment in computing, artificial intelligence, big and small data mining, group decision-making problems, pattern recognition, information processing, image processing, and many other practical achievements.

Neutrosophic optimization --- Neutrosophic medical diagnosis --- Neutrosophic data mining --- Neutrosophic operations used in MCDM --- NMCD Neutrosophic applications. --- Neutrosophic fault diagnosis --- Neutrosophic computational modelling --- Neutrosophic image processing using MCDM --- Neutrosophic clustering analysis --- Neutrosophic decision making theories and methods --- Artificial intelligence. --- Computer science. --- Informatics --- Science --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers

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This book constitutes the proceedings of the 10th International Conference on Health Information Science, HIS 2021, which took place in Melbourne, Australia, in October 2021. The 16 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 56 submissions. They are organized in topical sections named: COVID-19, EEG data processing, Medical Data Analysis, Medical Record Mining (I), Medical Data Mining (II), Medical Data Processing.

Social sciences (general) --- Programming --- Computer architecture. Operating systems --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- datamining --- applicatiebeheer --- apps --- informatica --- sociale wetenschappen --- programmeren (informatica) --- medische informatica --- database management --- architectuur (informatica) --- data acquisition

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This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders. This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features: ● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection. ● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders. ● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.

Medical informatics. --- Image processing—Digital techniques. --- Computer vision. --- Image processing. --- Machine learning. --- Artificial intelligence—Data processing. --- Health Informatics. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Image Processing. --- Machine Learning. --- Data Science. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Medicine --- Data processing --- Internal Medicine --- Medical

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This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders. This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features: ● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection. ● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders. ● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.

Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- DIP (documentimage processing) --- beeldverwerking --- grafische vormgeving --- programmeren (informatica) --- medische informatica --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie)