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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/25484/ http://umpir.ump.edu.my/id/eprint/25484/1/UMP%20IR%202%20MOHAMMED.PCC15015.FSKKP.pdf |
| _version_ | 1848822292622082048 |
|---|---|
| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-15T02:38:56Z |
| format | Article |
| id | ump-25484 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:38:56Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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. (Published) https://www.aconf.org/conf_99389.html |
| 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 |
| title | 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_short | Review of deep convolution neural network in image classification |
| title_sort | review of deep convolution neural network in image classification |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| 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 |