DAISY Descriptors Combined with Deep Learning to Diagnose Retinal Disease from High Resolution 2D OCT Images

Abstract

Optical Coherence Tomography (OCT) is commonly used to visualise tissue composition of the retina. Previously, deep learning has been used to analyse OCT images to automatically classify scans by the disease they display, however classification often requires downsampling to much lower dimensions. Downsampling often loses important features that may contain useful information. In this paper, a method is proposed which incorporates DAISY descriptors as ‘intelligent downsampling’. By avoiding random downsampling, we are able to keep more of the useful information to achieve more accurate results. The proposed method is tested on a publicly available dataset of OCT images, from patients with diabetic macula edema, drusen, and choroidal neovascularisation, as well as healthy patients. The method achieves an accuracy of 76.6% and an AUC of 0.935, this is an improvement to a previously used method which uses InceptionV3 with an accuracy of 67.8% and AUC of 0.912. This shows that DAISY descriptors do provide good representations of the image and can be used as an alternative to downsampling.