Residual Neural Network based Classification of Macular Edema in OCT

Macular edema is a retinal disease that may cause visual loss, even blindness. Both Subretinal fluid (SRF) and Pigment epithelium detachment(PED) are significant characteristics to help diagnose this disease. Optical coherence tomography (OCT) is a recognized technology for scanning retinal tissue, due to its non-invasive and high -resolution properties. Classification of SRF and PED among OCT images is thus the main task for macular edema diagnosis. General classification methods are based on classical machine learning applying domain specific knowledge for designing hand-crafted features. We proposed a residual network model to classify SRF and PED features among OCT images. In order to achieve better performance, data augmentation is investigated to respond to the challenge of data shortage. And fine-tuning Residual network from pre-trained parameters is applied. Since the task is a multi-label problem where a single data may have two labels, we also explored the potential correlations between these two labels. The large OCT dataset for training and evaluating model is provided by a competition platform called "AI Challenger". Experiments on the large-scale AI-Challenger OCT dataset demonstrate the effectiveness of the proposed approach. As a result, we achieve accuracies of 99.01% and 98.65% for the classification of SRF and PED, respectively.