Abstract：
Diabetes, characterized by heightened blood sugar levels, can lead to a condition called Diabetic Retinopathy (DR), which adversely impacts the eyes due to elevated blood sugar affecting the retinal blood vessels. The most common cause of blindness in diabetics is thought to be Diabetic Retinopathy (DR), particularly in working-age individuals living in poor nations. People with type 1 or type 2 diabetes may develop this illness, and the risk rises with the length of diabetes and inadequate blood sugar management. There are limits to traditional approaches for the early identification of diabetic retinopathy (DR). In order to diagnose diabetic retinopathy, a model based on Convolutional neural network (CNN) is used in a unique way in this research. The suggested model uses a number of deep learning (DL) models, such as VGG19, Resnet50, and InceptionV3, to extract features. After concatenation, these characteristics are sent through the CNN algorithm for classification. By combining the advantages of several models, ensemble approaches can be effective tools for detecting diabetic retinopathy and increase overall performance and resilience. Classification and image recognition are just a few of the tasks that may be accomplished with ensemble approaches like combination of VGG19,Inception V3 and Resnet 50 to achieve high accuracy. The proposed model is evaluated using a publicly accessible collection of fundus images.VGG19, ResNet50, and InceptionV3 differ in their neural network architectures, feature extraction capabilities, object detection methods, and approaches to retinal delineation. VGG19 may excel in capturing fine details, ResNet50 in recognizing complex patterns, and InceptionV3 in efficiently capturing multi-scale features. Their combined use in an ensemble approach can provide a comprehensive analysis of retinal images, aiding in the delineation of retinal regions and identification of abnormalities associated with diabetic retinopathy. For instance, micro aneurysms, the earliest signs of DR, often require precise detection of subtle vascular abnormalities. VGG19\'s proficiency in capturing fine details allows for the identification of these minute changes in retinal morphology. On the other hand, ResNet50\'s strength lies in recognizing intricate patterns, making it effective in detecting neoneovascularization and complex haemorrhagic lesions. Meanwhile, InceptionV3\'s multi-scale feature extraction enables comprehensive analysis, crucial for assessing macular oedema and ischaemic changes across different retinal layers.
摘要：
糖尿病,以血糖水平升高为特征，会导致一种叫做糖尿病视网膜病变（DR）的疾病，由于血糖升高影响视网膜血管而对眼睛产生不利影响。糖尿病患者失明的最常见原因被认为是糖尿病视网膜病变(DR)。特别是生活在贫穷国家的劳动年龄个人。患有1型或2型糖尿病的人可能会患上这种疾病，随着糖尿病的持续时间和血糖管理的不足，风险也会增加。早期识别糖尿病性视网膜病变(DR)的传统方法存在局限性。为了诊断糖尿病性视网膜病变，在这项研究中，基于卷积神经网络(CNN)的模型以一种独特的方式被使用。建议的模型使用了许多深度学习(DL)模型，例如VGG19、Resnet50和InceptionV3，以提取特征。串联后，这些特征通过CNN算法进行分类。通过结合几种模式的优点，集成方法可以成为检测糖尿病视网膜病变并提高整体性能和弹性的有效工具。分类和图像识别只是可以通过集成方法（如VGG19，InceptionV3和Resnet50的组合）来实现高精度的一些任务。使用可公开访问的眼底图像集合来评估所提出的模型。VGG19、ResNet50和InceptionV3的神经网络架构不同，特征提取功能，目标检测方法，和视网膜轮廓的方法。VGG19可能擅长捕捉细节，ResNet50在识别复杂模式中，和InceptionV3在有效地捕获多尺度特征。它们在集成方法中的组合使用可以提供视网膜图像的全面分析，帮助描绘视网膜区域和识别与糖尿病视网膜病变相关的异常。例如,微动脉瘤，最早的DR征象,通常需要精确检测细微的血管异常。VGG19在捕捉精细细节方面的熟练程度允许识别视网膜形态的这些微小变化。另一方面,ResNet50的优势在于识别复杂的模式，使其有效检测新血管形成和复杂的出血性病变。同时,InceptionV3的多尺度特征提取可以实现综合分析，对于评估不同视网膜层的黄斑水肿和缺血性变化至关重要。
