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Learning feature extractors for AMD classification in OCT using convolutional neural networks / Dafydd Ravenscroft; Jingjing Deng; Xianghua Xie; Louise Terry; Tom H. Margrain; Rachel V. North; Ashley Wood

Signal Processing Conference (EUSIPCO), 2017 25th European, Pages: 51 - 55

Swansea University Author: Xie, Xianghua

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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: College of Science
Start Page: 51
End Page: 55