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

Ravenscroft, Dafydd, Deng, Jingjing, Xie, Xianghua, Terry, Louise ORCID: https://orcid.org/0000-0002-6200-8230, Margrain, Tom H., North, Rachel V. and Wood, Ashley ORCID: https://orcid.org/0000-0002-9312-6184 2017. Learning feature extractors for AMD classification in OCT using convolutional neural networks. Presented at: 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, 28 August - 2 September 2017. 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, pp. 51-55. 10.23919/EUSIPCO.2017.8081167

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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. The proposed method is applied to a practical medical diagnosis problem of classifying different stages of Age-Related Macular Degeneration (AMD) using a dataset comprising long-wavelength Optical Coherence Tomography (OCT) images of the choroid. The experimental results show that the proposed method extracts more discriminative features than the features learnt through CNN only. It also suggests the feasibility of classifying different AMD stages using the textural information of the choroid region.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Optometry and Vision Sciences
Publisher:	IEEE
ISBN:	978-1-5386-0751-0
ISSN:	2076-1465
Last Modified:	24 Oct 2022 08:41
URI:	https://orca.cardiff.ac.uk/id/eprint/118433

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