Deep learning techniques for biomedical image classification
Deep convolutional neural networks (deep CNNs) is currently the state-of-the-art methods for image classification. In biomedical image field, deep learning can be used with transfer learning to address the problem of small training datasets. For deep CNNs that are pretrained on large image datase...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/75368 |
| _version_ | 1826120586570498048 |
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| author | Nguyen, Duc Long |
| author2 | Lin Zhiping |
| author_facet | Lin Zhiping Nguyen, Duc Long |
| author_sort | Nguyen, Duc Long |
| collection | NTU |
| description | Deep convolutional neural networks (deep CNNs) is currently the state-of-the-art
methods for image classification. In biomedical image field, deep learning can be used
with transfer learning to address the problem of small training datasets. For deep CNNs
that are pretrained on large image datasets, their layers are general enough to be
capable of generating useful features for an unseen biomedical dataset. In this research,
we propose various deep CNNs based on transfer learning technique. We come out
with a feature concatenation scheme that effectively exploit the power of deep neural
networks. We also experiment new technique such as ensemble learning for accuracy
improvement. The experiments on the 2D-Hela and the PAP-smear microscopic
datasets show that our proposed structures have significant performance improvements
over several traditional classification methods. Part of this project have been accepted
as a poster session in the IEEE International Symposium on Circuits and Systems
(ISCAS) 2018 conference. |
| first_indexed | 2024-10-01T05:18:53Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/75368 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:18:53Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/753682023-07-07T15:56:06Z Deep learning techniques for biomedical image classification Nguyen, Duc Long Lin Zhiping School of Electrical and Electronic Engineering DRNTU::Engineering DRNTU::Engineering::Electrical and electronic engineering Deep convolutional neural networks (deep CNNs) is currently the state-of-the-art methods for image classification. In biomedical image field, deep learning can be used with transfer learning to address the problem of small training datasets. For deep CNNs that are pretrained on large image datasets, their layers are general enough to be capable of generating useful features for an unseen biomedical dataset. In this research, we propose various deep CNNs based on transfer learning technique. We come out with a feature concatenation scheme that effectively exploit the power of deep neural networks. We also experiment new technique such as ensemble learning for accuracy improvement. The experiments on the 2D-Hela and the PAP-smear microscopic datasets show that our proposed structures have significant performance improvements over several traditional classification methods. Part of this project have been accepted as a poster session in the IEEE International Symposium on Circuits and Systems (ISCAS) 2018 conference. Bachelor of Engineering 2018-05-31T02:29:55Z 2018-05-31T02:29:55Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75368 en Nanyang Technological University 51 p. application/pdf |
| spellingShingle | DRNTU::Engineering DRNTU::Engineering::Electrical and electronic engineering Nguyen, Duc Long Deep learning techniques for biomedical image classification |
| title | Deep learning techniques for biomedical image classification |
| title_full | Deep learning techniques for biomedical image classification |
| title_fullStr | Deep learning techniques for biomedical image classification |
| title_full_unstemmed | Deep learning techniques for biomedical image classification |
| title_short | Deep learning techniques for biomedical image classification |
| title_sort | deep learning techniques for biomedical image classification |
| topic | DRNTU::Engineering DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/75368 |
| work_keys_str_mv | AT nguyenduclong deeplearningtechniquesforbiomedicalimageclassification |