Data mining for AMD screening: A classification based approach



Hijazi, MHA, Coenen, F ORCID: 0000-0003-1026-6649 and Zheng, Y ORCID: 0000-0002-7873-0922
(2014) Data mining for AMD screening: A classification based approach. International Journal of Simulation: Systems, Science and Technology, 15 (2). 57 - 69.

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Abstract

This paper investigates the use of three alternative approaches to classifying retinal images. The novelty of these approaches is that they are not founded on individual lesion segmentation for feature generation, instead use encodings focused on the entire image. Three different mechanisms for encoding retinal image data were considered: (i) time series, (ii) tabular and (iii) tree based representations. For the evaluation two publically available, retinal fundus image data sets were used. The evaluation was conducted in the context of Age-related Macular Degeneration (AMD) screening and according to statistical significance tests. Excellent results were produced: Sensitivity, specificity and accuracy rates of 99% and over were recorded, while the tree based approach has the best performance with a sensitivity of 99.5%. Further evaluation indicated that the results were statistically significant. The excellent results indicated that these classification systems are ideally suited to large scale AMD screening processes.

Item Type: Article
Uncontrolled Keywords: Age-related macular degeneration, Data mining, Decision support techniques, Classification, Retinal image
Depositing User: Symplectic Admin
Date Deposited: 22 Feb 2017 07:41
Last Modified: 23 Jan 2021 17:12
DOI: 10.5013/IJSSST.a.15.02.09
URI: https://livrepository.liverpool.ac.uk/id/eprint/3005971