Deep Learning Applications in Image Analysis
1st Edition
Published on 8. July 2023
XII, 210 pages
978-981-99-3784-4 (ISBN)
Description
This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3.
The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.
The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.
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Additional editions
Sanjiban Sekhar Roy | Ching-Hsien Hsu | Venkateshwara Kagita
Deep Learning Applications in Image Analysis
Book
07/2023
Springer
€267.49
Shipment within 3-4 weeks
Persons
Sanjiban Sekhar Roy is currently a Professor with the School of Computer Science and Engineering,Vellore Institute of Technology. He received Ph.D. degree from the Vellore Institute of Technology, Vellore, India, in 2016. He has edited handful of special issues for journals, published numerous articles in SCI high impact journals such as IEEE Transactions on Computational social systems; Scientific Reports,Nature; Computers and Electrical Engineering, Elsevier and many other reputed journals;Dr Roy has published nine books with reputed international publishers such as Springer, Elsevier and IGI Global. His research interests are deep learning and advanced machine learning.Dr. Roy was a recipient of the "Diploma of Excellence" Award for academic research from the Ministry of National Education, Romania. He was also an Associate Researcher with Ton Duc Thang University, Ho Chi Minh City, Vietnam, during 2019 to 2020.Ching-Hsien Hsu is Chair Professor of the College of Information and Electrical Engineering, Asia University, Taiwan; Professor in the department of Computer Science and Information Engineering, National Chung Cheng University; Research Consultant, Dept. of Medical Research, China Medical University Hospital, China Medical University, Taiwan. His research includes cloud and edge computing, big data analytics, high performance computing systems, parallel and distributed systems, artificial intelligence, medical AI and natural language processing. He has published 350+ papers in top journals such as IEEE TPDS, IEEE TSC, ACM TOMM, IEEE TCC, IEEE TETC, IEEE System, IEEE Network, top conferences, and book chapters in these areas. Dr. Hsu is the editor-in-chief of International Journal of Grid and High Performance Computing, and International Journal of Big Data Intelligence; and serving as editorial board for a number of prestigious journals, including IEEE Transactions on Service Computing, IEEE Transactions on Cloud Computing, International Journal of Cloud Computing, Journal of Communication Systems, International Journal of Computational Science, AutoSoft Journal. He has been acting as an author/co-author or an editor/co-editor of 10 books from Elsevier, Springer, IGI Global, World Scientific and McGraw-Hill. Dr. Hsu was awarded seven times talent awards from Ministry of Science and Technology, Ministry of Education, and nine times distinguished award for excellence in research from Chung Hua University, Taiwan. Prof. Hsu is president of Taiwan Association of Cloud Coputing; Chair of IEEE Technical Committee on Cloud Computing (TCCLD); Fellow of the IET (IEE) and senior member of the IEEE.
Venkateswara Rao Kagita is an Assistant Professor at NIT Warangal. He has obtained Ph.D from the University of Hyderabad. His research interests are Data Mining, Machine Learning, and Deep learning with a specific focus on machine learning techniques for recommender systems. His research works have been published in various reputed journals and conference proceedings. He has also delivered various guest lectures in several International and National workshops, IITs, NITs, and Universities.
Content
Classification and segmentation of images using deep learning.- Image reconstruction, image super-resolution and image synthesis by deep learning techniques.- Deep learning for cancer images.- Deep Learning in Gastrointestinal Endoscopy.- Tumor detection using deep learning.- Deep learning for image analysis using multimodality fusion.- Image quality recognition methods inspired by deep learning.- Advanced Deep Learning methods in computer vision with 3D data.- Deep Learning models to solve the task of MOT(Multiple Object Tracking).- Deep learning techniques for semantic segmentation of images.- Applications of deep learning for image forensics.- Human action recognition using deep learning.- Application of deep learning in satellite image classification and segmentation.
