Conference Paper/Proceeding/Abstract 1048 views 220 downloads
Learning feature extractors for AMD classification in OCT using convolutional neural networks
,
Louise Terry,
Tom H. Margrain,
Rachel V. North,
Ashley Wood
Signal Processing Conference (EUSIPCO), 2017 25th European, Pages: 51 - 55
Swansea University Authors:
Jingjing Deng, Xianghua Xie
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PDF | Accepted Manuscript
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DOI (Published version): 10.23919/EUSIPCO.2017.8081167
Abstract
In this paper, we propose a two-step textural feature extraction method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, and then generalizes the discriminative power by forming a histogram based descriptor.
| Published in: | Signal Processing Conference (EUSIPCO), 2017 25th European |
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| ISSN: | 2076-1465 |
| Published: |
Kos, Greece
Signal Processing Conference (EUSIPCO), 2017 25th European
2017
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa33957 |
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| Abstract: |
In this paper, we propose a two-step textural feature extraction method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, and then generalizes the discriminative power by forming a histogram based descriptor. |
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| College: |
Faculty of Science and Engineering |
| Start Page: |
51 |
| End Page: |
55 |