The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation

Abstract

We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation. OCT images were collected from patients with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, diabetic macular edema, retinal vein occlusion, cystoid macular edema in Irvine-Gass syndrome, and other macular diseases, along with the normal fellow eyes. A total of 14,327 OCT images were used to train DL models. Three experiments were conducted: classification alone (CA), use of automated segmentation of the OCT images by RelayNet, and the graph-cut technique before the classification (combination method 1 (CM1) and 2 (CM2), respectively). For validation of classification of the macular diseases, the sensitivity, specificity, and accuracy of CA were found at 62.55%, 95.16%, and 93.14%, respectively, whereas the sensitivity, specificity, and accuracy of CM1 were found at 72.90%, 96.20%, and 93.92%, respectively, and of CM2 at 71.36%, 96.42%, and 94.80%, respectively. The accuracy of CM2 was statistically higher than that of CA (p = 0.05878). All three methods achieved AUC at 97%. Applying DL for segmentation of OCT images prior to classification of the images by another DL model may improve the performance of the classification.

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

“…In [ 53 ], three different methods of using only one classifier, using a classifier and the segmentation output of the retinal layer and fluid segmentation network (RelayNet) model, and using a classifier and the segmentation output of the graph-cut method have been compared to classify OCT images in seven different classes and have shown the use of segmentation information leads to an increase in the accuracy of the final model. In [ 54 ], using an optimized algorithm for retina image segmentation and an ensemble structure of bagged trees and two deep learning models for 2D and 3D images, a classifier of SD-OCT images into non-, early, and intermediate AMD classes was introduced.…”