Deep learning applications in image analysis / Sanjiban Sekhar Roy, Ching-Hsien Hsu, Venkateshwara Kagita, editors.
Roy, Sanjiban Sekhar, editor.; Hsu, Ching-Hsien, editor.; Kagita, Venkateshwara, editor.
2023
TA1637
Available Online

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Title
Deep learning applications in image analysis / Sanjiban Sekhar Roy, Ching-Hsien Hsu, Venkateshwara Kagita, editors.
ISBN
9789819937844 (electronic bk.)
9819937841 (electronic bk.)
9819937833
9789819937837
DOI
https://doi.org/10.1007/978-981-99-3784-4
Published
Singapore : Springer, [2023]
Copyright
©2023
Language
English
Description
1 online resource (xii, 210 pages) : illustrations (some color).
Item Number
10.1007/978-981-99-3784-4 doi
Call Number
TA1637
Dewey Decimal Classification
621.36/7
Summary
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.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Roy, Sanjiban Sekhar, editor.
Hsu, Ching-Hsien, editor.
Kagita, Venkateshwara, editor.
Series
Studies in big data ; v. 129.
Available in Other Form
DEEP LEARNING APPLICATIONS IN IMAGE ANALYSIS.
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