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Conference Paper/Proceeding/Abstract 1178 views 242 downloads

Learning feature extractors for AMD classification in OCT using convolutional neural networks

Dafydd Ravenscroft, Jingjing Deng, Xianghua Xie Orcid Logo, 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 Orcid Logo

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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.

Published in: Signal Processing Conference (EUSIPCO), 2017 25th European
ISSN: 2076-1465
Published: Kos, Greece Signal Processing Conference (EUSIPCO), 2017 25th European 2017
Online Access: Check full text

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.
College: Faculty of Science and Engineering
Start Page: 51
End Page: 55