A. Shanthini, Gunasekaran Manogaran, G. Vadivu

Deep Convolutional Neural Network for The Prognosis of Diabetic Retinopathy (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book discusses a detailed overview of diabetic retinopathy, symptoms, causes, and screening methodologies. Using a deep convolution neural network and visualizations techniques, this work develops a prognosis system used to automatically detect the diabetic retinopathy disease from captured retina images and help improve the prediction rate of diagnosis. This book gives the readers an understanding of the diabetic retinopathy disease and recognition process that helps to improve the clinical analysis efficiency. It caters to general ophthalmologists and optometrists, diabetologists, and internists who encounter diabetic patients and most prevalent retinal diseases daily.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 75
• Erscheinungstermin: 23. August 2022
• Englisch
• ISBN-13: 9789811938771
• Artikelnr.: 65440776

Autorenporträt

Dr. A. Shanthini is currently working as Associate Professor in the Department of Data science and Business systems, SRM Institute of Science and Technology, Kattankulathur Campus, India. She received her Bachelor of Engineering, Master of Engineering and Ph.D. from Annamalai University, India. Her current research interests include Data analyticssh, machine learning, and deep learning in health care. She published two patents in the Patent Of¿ce Journal and one in Australian patent in the year 2018 and 2020, respectively. Currently, she is Principal Investigator of the project titled "Prognosis of Microaneurysm, and early diagnosis system for non-proliferative Diabetic Retinopathy using Deep Convolutional neural network" sponsored by SPARC-IITK, MHRD, Government of India, which is associated with University of California, Davis Campus, USA, and SRM IST, India, for 67 Lakhs in March 2019. She is author/co-author in 12 research articles in international journals and conferences,including SCI and Scopus indexed papers. She is Active Member of IEEE, ACM, and ISC.

Dr. Gunasekaran Manogaran is currently working as Big Data Scientist at University of California, Davis, USA. He is also Adjunct Assistant Professor, Department of Computer Science and Information Engineering, Asia University, Taiwan, and Adjunct Faculty, in School of Computing, SRM Institute of Science and Technology, Kattankulathur, India. He is Visiting Researcher/Scientist at the University of La Frontera, Colombia, and the International University of La Rioja, Spain. He received his Ph.D. from the Vellore Institute of Technology University, India. He received his Bachelor of Engineering and Master of Technology from Anna University, India, and Vellore Institute of Technology University, India, respectively. He is author/co-author of more than 100 papers in conferences, book chapters, and journals, including IEEE Transactions on Industrial Informatics, IEEE Transactions on Computational Social Systems, IEEE Internet of Things, IEEE Intelligent System, IEEE Access, ACM Transactions on Multimedia Computing, Communications, and Applications.

Dr. G. Vadivu is working in the teaching profession for more than two decades. Currently, she is designated Professor and Program Coordinator in the Department of Data Science and Business Systems at SRM Institute of Science and Technology, Kattankulathur Campus, India. Her research areas include big data analytics, semantic web, data mining, and database systems. She published more than 30 research articles in a reputed journal listed in SCIE and Scopus. She has organized UGC sponsored workshop on .NET Technologies during 2006 and 2009. She received the Best Teaching Faculty Award and Certi¿cate of Appreciation for the journal published in the year 2012. Also, she has completed Oracle Certi¿cation, Certi¿cation in Database Administration-Microsoft Technology Association, High-Impact Teaching Skills Certi¿ed by Dale Carnegie, and IBM-DB2 certi¿cation.

Inhaltsangabe

Introduction.- Chapter 1 - Background of diabetic retinopathy.- Chapter 2 - Classification of diabetic retinopathy.- Chapter 3 - Deep convolutional neural network architecture.- Chapter 4 - Deep convolutional neural network applications and visualization tools.- Chapter 5 - Multi-platform deployment for prognosis system.- Chapter 6 - Case Studies for diabetic retinopathy with a deep learning system.