Biomedical image classification based on a feature concatenation and ensemble of deep CNNs
Deep learning and more specifically Convolutional Neural Network (CNN) is a cutting edge technique which has been applied to many fields including biomedical image classification. To further improve the classification performance for biomedical images, in this paper, a feature concatenation method a...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/146840 |
| _version_ | 1811688847850864640 |
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| author | Nguyen, Long D. Gao, Ruihan Lin, Dongyun Lin, Zhiping |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Nguyen, Long D. Gao, Ruihan Lin, Dongyun Lin, Zhiping |
| author_sort | Nguyen, Long D. |
| collection | NTU |
| description | Deep learning and more specifically Convolutional Neural Network (CNN) is a cutting edge technique which has been applied to many fields including biomedical image classification. To further improve the classification performance for biomedical images, in this paper, a feature concatenation method and a feature concatenation and ensemble method are proposed to combine several CNNs with different depths and structures. Three datasets, namely 2D Hela dataset, PAP smear dataset, and Hep-2 cell image dataset, are used as benchmarks for testing the proposed methods. It is shown from experiments that the feature concatenation and ensemble method outperforms each individual CNN, and the feature concatenation method, as well as several state-of-the-art methods in terms of classification accuracy. |
| first_indexed | 2024-10-01T05:38:43Z |
| format | Journal Article |
| id | ntu-10356/146840 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:38:43Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1468402022-07-28T08:12:57Z Biomedical image classification based on a feature concatenation and ensemble of deep CNNs Nguyen, Long D. Gao, Ruihan Lin, Dongyun Lin, Zhiping School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Deep Convolutional Neural Network Transfer Learning Deep learning and more specifically Convolutional Neural Network (CNN) is a cutting edge technique which has been applied to many fields including biomedical image classification. To further improve the classification performance for biomedical images, in this paper, a feature concatenation method and a feature concatenation and ensemble method are proposed to combine several CNNs with different depths and structures. Three datasets, namely 2D Hela dataset, PAP smear dataset, and Hep-2 cell image dataset, are used as benchmarks for testing the proposed methods. It is shown from experiments that the feature concatenation and ensemble method outperforms each individual CNN, and the feature concatenation method, as well as several state-of-the-art methods in terms of classification accuracy. Nanyang Technological University Accepted version We wish to acknowledge the funding support for this project from Nanyang Technological University under the Undergraduate Research Experience on Campus (URECA) program. 2021-06-07T09:20:01Z 2021-06-07T09:20:01Z 2019 Journal Article Nguyen, L. D., Gao, R., Lin, D. & Lin, Z. (2019). Biomedical image classification based on a feature concatenation and ensemble of deep CNNs. Journal of Ambient Intelligence and Humanized Computing. https://dx.doi.org/10.1007/s12652-019-01276-4 1868-5137 0000-0002-1587-1226 https://hdl.handle.net/10356/146840 10.1007/s12652-019-01276-4 2-s2.0-85063219157 en Journal of Ambient Intelligence and Humanized Computing © 2019 Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved. This paper was published in Journal of Ambient Intelligence and Humanized Computing and is made available with permission of Springer-Verlag GmbH Germany, part of Springer Nature. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Deep Convolutional Neural Network Transfer Learning Nguyen, Long D. Gao, Ruihan Lin, Dongyun Lin, Zhiping Biomedical image classification based on a feature concatenation and ensemble of deep CNNs |
| title | Biomedical image classification based on a feature concatenation and ensemble of deep CNNs |
| title_full | Biomedical image classification based on a feature concatenation and ensemble of deep CNNs |
| title_fullStr | Biomedical image classification based on a feature concatenation and ensemble of deep CNNs |
| title_full_unstemmed | Biomedical image classification based on a feature concatenation and ensemble of deep CNNs |
| title_short | Biomedical image classification based on a feature concatenation and ensemble of deep CNNs |
| title_sort | biomedical image classification based on a feature concatenation and ensemble of deep cnns |
| topic | Engineering::Electrical and electronic engineering Deep Convolutional Neural Network Transfer Learning |
| url | https://hdl.handle.net/10356/146840 |
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