1st Edition
Deep Learning in Diabetes Mellitus Detection and Diagnosis
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 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. The 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.
- Assist 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.
- The subject's coverage is exceptional and includes the principles needed to understand deep learning.
Chapter 1: Introduction to Diabetes Mellitus Detection and Diagnosis using Deep Learning
Jyotismita Chaki and Dibyajyoti Ghosh
Chapter 2: Pre-processing and Detection of Diabetes Mellitus from physiological Data using Deep Learning
Muskan Sah, Prajwal Kulkarni, Pranav Sehgal, and Akila Victor
Chapter 3: Graph-based Explainable Method for Blood Glucose Prediction through Federated Learning
Chengzhe Piao and Kezhi Li
Chapter 4: Automated Early Detection of Diabetes Mellitus from Retinal Fundus images using Residual U-Network Approach
K. Sujatha, R.S. Ponmagal, N. Janaki, NPG. Bhavani, and SuQun Cao
Chapter 5: Towards Classifying the Severity of Diabetic Retinopathy Using Deep Learning
Ayush Negi, Shrish Srinivasan, and Bhuvaneswari Amma N. G.
Chapter 6: Deep Learning saves lives of Diabetes Mellitus Patients and cuts treatment costs
D. Kavitha, Ravindra Changala, S V Suresh Babu Matla, Kanakaprabha. S, Lella Kranthi Kumar, and G. Ganesh Kumar
Chapter 7: Diabetes Mellitus Detection using Deep Learning Model
M. Karthikeyan and R. Rengaraj
Chapter 8: A Comprehensive Review of the Use of Deep Learning Algorithms in Diabetes Mellitus Detection and Diagnosis
Katarzyna Wiltos
Chapter 9: Examining the Role of Machine Learning and Deep Learning in Diabetes Mellitus Detection and Diagnosis: A Critical Review
Gunavathi C, Akshat Bokdia, and Siri R Kulakarni
Chapter 10: Deep Learning in Diabetes Mellitus Detection and Diagnosis
Atianashie A. Miracle and Chukwuma Chinaza Adaobi
Chapter 11: Title: Deep Learning Algorithms for Diabetes Mellitus Detection and Management
Mohadeseh Zarei Ghobadi, Mohammad Dehghani, and Elaheh Afsaneh
Chapter 12: An Analysis of Deep Learning Models for Diabetic Retinopathy Detection and Classification Based on Fundus Image
Govind Narayan Patel, LipikaDinda, Jitesh Pradhan, and B Ramachandra Reddy
Biography
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.
