Scopus, IGI Global, & Web of Science call for book chapters for a new publication: “Deep Learning Approaches for Early Diagnosis of Neurodegenerative Disease”. The main objective of “Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases” is to provide a comprehensive resource that explores the integration of deep learning methodologies with neuroscience for the early detection of neurodegenerative disorders. By assembling a diverse range of research contributions, this book aims to present state-of-the-art deep learning techniques tailored to neurodegenerative disease diagnosis while bridging the gap between AI experts and neurologists, fostering interdisciplinary collaboration. Moreover, the book endeavors to offer insights into the development of accurate, non-invasive, and cost-effective diagnostic tools, showcasing practical applications of deep learning in clinical settings, and thereby enhancing disease management. Through these contributions, the book seeks to make a valuable addition to current research efforts by promoting novel approaches and potential breakthroughs, ultimately advancing the field of early diagnosis for neurodegenerative.
Call for Chapters “Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases” Deadlines
- August 20, 2023: Proposal Submission Deadline
- September 3, 2023: Notification of Acceptance
- 25, October 2023: Last date for submission of full paper
- 3, November 2023: Acceptance of the paper
- November 5, 2023: Full Chapter Submission
- December 17, 2023: Review Results Returned
- January 14, 2024: Final Acceptance Notification
- January 21, 2024: Final Chapter Submission
Target Audience
The primary audience for “Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases” includes researchers, clinicians, and professionals in the fields of neurology, artificial intelligence, machine learning, and biomedical engineering. It will also be beneficial for graduate students and postdoctoral researchers working on neurodegenerative diseases, as well as industry professionals interested in developing diagnostic tools and technologies. The book caters to individuals who seek a comprehensive understanding of the applications of deep learning in early disease detection and wish to contribute to advancements in the field. Additionally, policymakers and healthcare administrators focused on improving diagnostic practices and patient care for neurodegenerative disorders will find this publication informative and thought-provoking.
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