Application of deep learning for retinal image analysis: A review

Retinal image analysis holds an imperative position for the identification and classification of retinal diseases such as Diabetic Retinopathy (DR), Age Related Macular Degeneration (AMD), Macular Bunker, Retinoblastoma, Retinal Detachment, and Retinitis Pigmentosa. Automated identification of retinal diseases is a big step towards early diagnosis and prevention of exacerbation of the disease. A number of state-of-the-art methods have been developed in the past that helped in the automatic segmentation and identification of retinal landmarks and pathologies. However, the current unprecedented advancements in deep learning and modern imaging modalities in ophthalmology have opened a whole new arena for researchers. This paper is a review of deep learning techniques applied to 2-D fundus and 3-D Optical Coherence Tomography (OCT) retinal images for automated classification of retinal landmarks, pathology, and disease classification. The methodologies are analyzed in terms of sensitivity, specificity, Area under ROC curve, accuracy, and F score on publicly available datasets which includes DRIVE, STARE, CHASE_DB1, DRiDB, NIH AREDS, ARIA, MESSIDOR-2, E-OPTHA, EyePACS-1 DIARETDB and OCT image datasets. (C) 2019 Elsevier Inc. All rights reserved.