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.

视网膜图像分析在诸如糖尿病性视网膜病变（DR），与年龄有关的黄斑变性（AMD），黄斑ker堡，视网膜母细胞瘤，视网膜脱离和视网膜色素变性的视网膜疾病的识别和分类中具有至关重要的地位。自动化识别视网膜疾病是朝着早期诊断和预防疾病恶化迈出的一大步。过去已经开发了许多先进的方法，这些方法有助于自动分割和识别视网膜界标和病理。但是，眼科领域目前在深度学习和现代成像方式方面空前的进步为研究人员打开了一个全新的舞台。本文是对应用于2D眼底和3D光学相干断层扫描（OCT）视网膜图像的深度学习技术的综述，以对视网膜标志物，病理和疾病分类进行自动分类。在敏感性，特异性，ROC曲线下面积，准确性和F得分等公开可用数据集上对方法进行了分析，这些数据集包括DRIVE，STARE，CHASE_DB1，DRiDB，NIH AREDS，ARIA，MESSIDOR-2，E-OPTHA，EyePACS- 1 DIARETDB和OCT图像数据集。