Dubey Shradha, Dixit Manish
Madhav Institute of Technology & Science (Department of Computer Science and Engineering), Gwalior, M.P. India.
Multimed Tools Appl. 2023;82(10):14471-14525. doi: 10.1007/s11042-022-13841-9. Epub 2022 Sep 24.
Diabetes is a long-term condition in which the pancreas quits producing insulin or the body's insulin isn't utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.
糖尿病是一种长期疾病，胰腺停止分泌胰岛素或身体无法正常利用胰岛素。糖尿病性视网膜病变是糖尿病的症状之一。糖尿病性视网膜病变是最常见的糖尿病类型，如果不加以治疗，糖尿病性视网膜病变会影响所有糖尿病患者，并变得非常严重，增加失明的几率。它是一种慢性全身性疾病，超过80%的患者患病超过十年。许多研究人员认为，如果糖尿病患者能尽早被诊断出来，90%的病例可以得到治愈。糖尿病会损害视网膜中的微小血管——毛细血管。在图像上，血管损伤通常很明显。因此，在本研究中，我们回顾了几种传统方法以及基于深度学习的方法，用于对这种特定的基于糖尿病的眼部疾病——糖尿病性视网膜病变进行分类和检测，并描述了一种方法相对于另一种方法的优势。除了这些方法，还讨论了用于糖尿病性视网膜病变检测和分类的数据集和评估指标。本研究的主要发现是让研究人员了解在使用计算机视觉、深度学习技术检测糖尿病性视网膜病变时出现的不同挑战。因此，这篇综述论文的目的是总结在检测糖尿病性视网膜病变时的所有主要方面，如病变识别、分类和分割、深度学习模型的安全攻击、数据集的正确分类和评估指标。由于深度学习模型成本高昂且更容易受到安全攻击，因此，未来建议开发一种精细、可靠且强大的模型，该模型能够克服在设计深度学习模型时常见的所有这些方面的问题。
