Retinopathy screening from OCT imagery via deep learning

Abstract

Optical coherence tomography (OCT) is a noninvasive ophthalmic technique used to diagnose different retinal diseases based on image texture and geometric features. Manual processing of OCT scans is time consuming and operator-dependent and might limit early prognosis and medication for eye conditions; hence, there is a need for automated methods. This chapter presents a deep learning framework to discern four forms of retinal degeneration. The proposed model is a lightweight network that combines the atrous spatial pyramid pooling (ASPP) mechanism with deep residual learning. Based on the shortcut connections and dilated atrous convolutions in ASPP, our model exploits the multiscale retinal features in OCT scans for disease prediction. We used 108,309 OCT scans to train the model extensively and 1000 OCT scans to test its performance. The simulation results reveal that our method outperforms other cutting-edge approaches in a multiclass classification task, achieving an accuracy of 98.90% with a true positive rate of 97.80% and a true negative rate (TNR) of 99.27%.

Keywords: Age-Related Macular Degeneration (AMD), Choroidal Neovascularization (CNV), Classification, Deep Learning, Diabetic Macular Edema (DME), OCT Imaging, Retinal Diseases