Deep Learning in Diabetes Mellitus Detection and Diagnosis
Herausgeber: Chaki, Jyotismita; Wozniak, Marcin
Schade – dieser Artikel ist leider ausverkauft. Sobald wir wissen, ob und wann der Artikel wieder verfügbar ist, informieren wir Sie an dieser Stelle.
Deep Learning in Diabetes Mellitus Detection and Diagnosis
Herausgeber: Chaki, Jyotismita; Wozniak, Marcin
- Gebundenes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
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.
Andere Kunden interessierten sich auch für
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.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 232
- Erscheinungstermin: 9. Januar 2025
- Englisch
- Abmessung: 234mm x 156mm
- ISBN-13: 9781032647005
- ISBN-10: 1032647000
- Artikelnr.: 71299526
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 232
- Erscheinungstermin: 9. Januar 2025
- Englisch
- Abmessung: 234mm x 156mm
- ISBN-13: 9781032647005
- ISBN-10: 1032647000
- Artikelnr.: 71299526
Jyotismita Chaki, PhD, is an Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. She earned her PhD (Engg.) from Jadavpur University, Kolkata, India. Her research interests include computer vision and image processing, pattern recognition, medical imaging, artificial intelligence, and machine learning. Jyotismita has authored more than 40 international conference and journal papers and is the author and editor of more than eight books. Currently, she is the Academic Editor of PLOS One journal and PeerJ Computer Science journal and Associate Editor of IET Image Processing journal, Array journal, and Machine Learning with Applications journal. Marcin Wozniak received the M.Sc. degree in applied mathematics, the Ph.D. degree in computational intelligence, the D.Sc. degree in computational intelligence and Full Professor honours from the President of Poland. M. Wozniak is currently a Full Professor with the Faculty of Applied Mathematics, Silesian University of Technology. He is a Scientific Supervisor in editions of "The Diamond Grant" and "The Best of the Best" programs for highly talented students from the Polish Ministry of Science and Higher Education. He participated in various scientific projects (as Lead Investigator, Scientific Investigator, Manager, Participant and Advisor) at Polish, Italian and Lithuanian universities and projects with applied results at IT industry both funded from the National Centre for Research and Development and abroad. He was a Visiting Researcher with universities in Italy, Sweden, and Germany. He has authored/coauthored over 300 research papers in international conferences and journals. His current research interests include neural networks with their applications together with various aspects of fuzzy logic and control, applied computational intelligence accelerated by evolutionary computation and federated learning models. In 2017 Prof Marcin Wozniak was awarded by the Polish Ministry of Science and Higher Education with a scholarship for an outstanding young scientist. In years 2021 and 2024 he received two awards from the Polish Ministry of Science and Higher Education for research achievements. In 2020, 2021, 2022 and 2023 Prof Marcin Wozniak was presented among "TOP 2% Scientists in the World" by Stanford University for his career achievements. Prof Marcin Wozniak is also presented among the Best Computer Science Scientists in Poland by Research.com. Marcin Wozniak is the Editorial Board member or an Editor for Biomedical Signal Processing and Control, Sensors, Machine Learning with Applications, Pattern Analysis and Applications. He also guest edit special issues ie. IEEE Journal Biomedical and Health Informatics, IEEE ACCESS, Measurement, Sustainable Energy Technologies and Assessments, Frontiers in Human Neuroscience, PeerJ CS, International Journal of Distributed Sensor Networks, Computational Intelligence and Neuroscience, Journal of Universal Computer Science, etc. Prof. Marcin Wozniak is a Session Chair at various international conferences and symposiums, including IEEE Symposium Series on Computational Intelligence, IEEE Congress on Evolutionary Computation, International Joint Conference on Neural Networks, etc.
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.
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.
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.
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.