Data-Sparse Deep Learning for Studying Diabetic Retinopathy Lesions

Diabetic retinopathy, a serious complication of diabetes, can result in permanent blindness if not promptly detected. The difficulty in assessing the stages of the disease using fundus images by experienced human specialists impedes early identification. This problem has prompted researchers to turn toward convolutional neural networks (CNNs) to simplify the evaluation process. The incorporation of CNNs offers hope for a more effective and accurate distinguishing of the stages of diabetic retinopathy, which could help prevent permanent loss of vision. The objective of such an effective screening mechanism is to simplify procedures, and thus allow for early intervention soon after onset so that the precious gift of sight will not be lost. We suggest a classification system below along these lines.