A Systematic Review on Deep Learning Techniques for Diabetic Retinopathy Segmentation and Detection Using Ocular Imaging Modalities

Diabetic Retinopathy (DR) is a rapidly growing consequence of diabetes mellitus globally. DR causes lesions that can cause blindness if untreated. The significant advancement in deep learning (DL) approaches have proven to be superior to traditional detection methods. This systematic review provides a comprehensive overview of development of DL based approach for DR segmentation and detection (SD) through ocular imaging that help ophthalmologists diagnose DR at early stage. Advances in ocular imaging has developed its contribution towards early detection of DR. Articles on ocular imaging for SD of DR were identified by following PRISMA guidelines using query “Deep Learning”, “Diabetic Retinopathy”, “retinal imaging” alone and in combination in PubMed, Google Scholar, IEEE Xplore, and Research Gate databases until 2021. Approximately 1000 publications were searched and 153 relevant studies focused on the DL approaches for SD of utilizing ocular imaging were chosen for study. According to the survey, 66% of researchers employed DL approaches for Blood vessel (BV) segmentation, 36% of researchers used DL approaches for lesion detection, 15% of researchers have used DL approaches for optic disc and optic cup (OD and OC) segmentation for DR Diagnosis. This systematic review provided detailed literature of the state of the art relevant articles for SD of BV, Lesions, OD and OC for non-proliferative DR diagnosis at the early stage and discusses future directions to improve the performance of DL approaches for DR diagnosis and to overcome research challenges. Finally, this article highlights the outline of the proposed work to improve the accuracy of existing models.