Review of deep convolution neural network in image classification

With the development of large data age, Convolutional neural networks (CNNs) with more hidden layers have more complex network structure and more powerful feature learning and feature expression abilities than traditional machine learning methods. The convolution neural network model trained by the...

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Main Authors:	Al-Saffar, Ahmed Ali Mohammed, Tao, Hai, Mohammed, Ahmed Talab
Format:	Article
Language:	English
Published:	IEEE 2017
Subjects:	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25484/ http://umpir.ump.edu.my/id/eprint/25484/ http://umpir.ump.edu.my/id/eprint/25484/1/UMP%20IR%202%20MOHAMMED.PCC15015.FSKKP.pdf

id	ump-25484
recordtype	eprints
spelling	ump-254842019-12-17T06:40:30Z http://umpir.ump.edu.my/id/eprint/25484/ Review of deep convolution neural network in image classification Al-Saffar, Ahmed Ali Mohammed Tao, Hai Mohammed, Ahmed Talab T Technology (General) TK Electrical engineering. Electronics Nuclear engineering With the development of large data age, Convolutional neural networks (CNNs) with more hidden layers have more complex network structure and more powerful feature learning and feature expression abilities than traditional machine learning methods. The convolution neural network model trained by the deep learning algorithm has made remarkable achievements in many large-scale identification tasks in the field of computer vision since its introduction. This paper first introduces the rise and development of deep learning and convolution neural network, and summarizes the basic model structure, convolution feature extraction and pooling operation of convolution neural network. Then, the research status and development trend of convolution neural network model based on deep learning in image classification are reviewed, which is mainly introduced from the aspects of typical network structure construction, training method and performance. Finally, some problems in the current research are briefly summarized and discussed, and the new direction of future development is forecasted IEEE 2017 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25484/1/UMP%20IR%202%20MOHAMMED.PCC15015.FSKKP.pdf Al-Saffar, Ahmed Ali Mohammed and Tao, Hai and Mohammed, Ahmed Talab (2017) Review of deep convolution neural network in image classification. International Conference on Radar Antenna, Microwave, Electronics, and Telecommunications. pp. 26-31. ISSN 978-1-5386-3849 https://www.aconf.org/conf_99389.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Al-Saffar, Ahmed Ali Mohammed Tao, Hai Mohammed, Ahmed Talab Review of deep convolution neural network in image classification
description	With the development of large data age, Convolutional neural networks (CNNs) with more hidden layers have more complex network structure and more powerful feature learning and feature expression abilities than traditional machine learning methods. The convolution neural network model trained by the deep learning algorithm has made remarkable achievements in many large-scale identification tasks in the field of computer vision since its introduction. This paper first introduces the rise and development of deep learning and convolution neural network, and summarizes the basic model structure, convolution feature extraction and pooling operation of convolution neural network. Then, the research status and development trend of convolution neural network model based on deep learning in image classification are reviewed, which is mainly introduced from the aspects of typical network structure construction, training method and performance. Finally, some problems in the current research are briefly summarized and discussed, and the new direction of future development is forecasted
format	Article
author	Al-Saffar, Ahmed Ali Mohammed Tao, Hai Mohammed, Ahmed Talab
author_facet	Al-Saffar, Ahmed Ali Mohammed Tao, Hai Mohammed, Ahmed Talab
author_sort	Al-Saffar, Ahmed Ali Mohammed
title	Review of deep convolution neural network in image classification
title_short	Review of deep convolution neural network in image classification
title_full	Review of deep convolution neural network in image classification
title_fullStr	Review of deep convolution neural network in image classification
title_full_unstemmed	Review of deep convolution neural network in image classification
title_sort	review of deep convolution neural network in image classification
publisher	IEEE
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/25484/ http://umpir.ump.edu.my/id/eprint/25484/ http://umpir.ump.edu.my/id/eprint/25484/1/UMP%20IR%202%20MOHAMMED.PCC15015.FSKKP.pdf
first_indexed	2023-09-18T22:39:09Z
last_indexed	2023-09-18T22:39:09Z
_version_	1777416798115725312

Review of deep convolution neural network in image classification

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