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Deep Learning in Diabetes Mellitus Detection and Diagnosis

Edited by Jyotismita Chaki, Edited by Marcin Wozniak

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Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning. Features: Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosis Assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equations Involves exceptional subject coverage and includes the principles needed to understand deep learning

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