Deep Learning in Diabetes Mellitus Detection and Diagnosis
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 30 January 2025
- ISBN 9781032647005
- Binding Hardback
- No. of pages200 pages
- Size 234x156 mm
- Weight 530 g
- Language English
- Illustrations 54 Illustrations, black & white; 54 Line drawings, black & white 683
Categories
Short description:
This book focuses on deep learning-based approaches in the field of Diabetes Mellitus detection and diagnosis, including preprocessing techniques which 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.
MoreLong description:
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
Table of Contents:
1. Introduction to Diabetes Mellitus Detection and Diagnosis using deep Learning. 2. Pre-processing and Detection of Diabetes Mellitus from physiological data using deep learning. 3. Graph-based Explainable Method for Blood Glucose Prediction through Federated Learning. 4. Automated Early detection of Diabetes Mellitus from Retinal Fundus images using Residual U-Network Approach. 5. Towards Classifying the Severity of Diabetic Retinopathy Using Deep Learning. 6. Deep Learning saves lives of diabetes mellitus patients and cuts treatment costs. 7. Diabetes mellitus detection using deep learning model. 8. A Comprehensive Review of the Use of Deep Learning Algorithms in Diabetes Mellitus Detection and Diagnosis. 9. Examining the Role of Machine Learning and Deep Learning in Diabetes Mellitus Detection and Diagnosis - A Critical Review. 10. Deep Learning in Diabetes Mellitus Detection and Diagnosis. 11. Title: Deep Learning Algorithms for Diabetes Mellitus Detection and Management. 12. An Analysis of Deep Learning Models for Diabetic Retinopathy Detection and Classification Based on Fundus Image.
