Data Analysis for Neurodegenerative Disorders (Cognitive Technologies) (EPUB)

This book discusses the challenges associated with administrating large amounts of medical information in the diagnosis of neurological diseases. It talks about how to effectively reduce the number of neuropsychological tests during the classification of these diseases by utilizing feature selection methods based on the diagnostic information of the participants. The book provides essential information and models regarding their applications in various types of signal and image processing for the purpose of neurological data. A comprehensive analysis of the potential for enhancing the capabilities of AI systems through different data analyses is also included. The book discusses fundamental principles and models of different AI networks and processes, as well as the basic processing of images associated with various neurological diseases. Additionally, the book discusses the applications of image processing, and it explores the role of large data analysis in addressing the issues of signal and image processing in real-world scenarios involving neurological disorders.

This innovative book describes the procedure of analyzing medical data and introducing new methods and issues in the handling of neurological signals and images. It will be beneficial for scientists, researchers, and developers who want to understand the concepts of AI, as well as the different models and data analysis. To comprehensively explore the topic, the book focuses on the three primary aspects.

● Provides innovative ideas and models for using AI in practical applications for neurological diseases, these applications are accompanied by detailed descriptions of how images are represented, how features are extracted, and how they are selected.

● Highlights various methods of evaluating the effectiveness of computer-aided diagnosis (CAD) systems for neurological diseases.

● Investigates different methods of signal and image processing for effective decision support systems, including soft computing, machine learning, and optimization, all of which are incorporated into CAD systems to enhance their efficiency.