Diabetic retinopathy (DR) is one of the leading causes of vision loss. It causes neovascularization with blocking the regular small blood vessels. Early detection helps the ophthalmologist in patient treatment and prevents or delays vision loss.

In this work, multifractal analysis has been used in some details to automate the diagnosis of diabetic without diabetic retinopathy and non-proliferative DR. Concerning using number of multifractal geometrical methods, as a necessary second step the enforcement of the sophisticated artificial neural network has been consultant in order to improve the accuracy of the obtained results.

Patients and methods: Thirty normal cases’ eyes, 30 diabetic without DR patients’ eyes and 30 non-proliferative diabetic retinopathy (mild to moderate) eyes are exposed to optical coherence tomography angiography (OCTA) to get image superficial layer of macula for all cases. These images were approved in Ophthalmology Center in Mansoura University, Egypt, and medically were diagnosed by the ophthalmologists. We extract the most changeable features that associated to the morphological retinal vascular network alternations. The seven extracted features are related to the multifractal analysis results, which describe the vascular network architecture and gaps distribution. A supervised Artificial Neural Network (ANN) is used to classify the images into three categories: normal, diabetic without diabetic retinopathy and non-proliferative DR.

Results: The human retinal blood vascular network architecture is found to be a fractal system. Multifractal geometry describes the irregularity and gaps distribution in the retina. We extracted seven features from the studied images. The features were the generalized dimensions D₀ , D₁ , D₂ , α at the maximum f(α) singularity spectrum, the spectrum width, the spectrum symmetrical shift point and lacunarity. The ANN obtains a single value decision with classification accuracy 97.78%, with minimum sensitivity 96.67%.

Conclusion: Early stages of DR could be noninvasively detected using high-resolution OCTA images that were analysed by multifractal geometry parameterization and implemented by the sophisticated artificial neural network with classification accuracy 96.67%. This approach could promote risk stratification for the decision of early diagnosis of diabetic retinopathy.

糖尿病性视网膜病（DR）是视力丧失的主要原因之一。它会导致新血管形成并阻塞正常的小血管。早期检测有助于眼科医生进行患者治疗，并预防或延迟视力丧失。

在这项工作中，多重分形分析已在某些细节上用于自动诊断无糖尿病性视网膜病变和非增殖性DR的糖尿病。关于使用多种分形几何方法，作为必要的第二步骤，已经咨询了复杂的人工神经网络的实施，以提高所获得结果的准确性。

患者和方法：将30例正常眼，30例无DR患者的糖尿病眼和30例非增生性糖尿病视网膜病变（轻至中度）眼暴露于光学相干断层扫描血管造影（OCTA），以获取所有病例的黄斑图像表层。这些图像在埃及曼苏拉大学眼科中心得到批准，并在医学上由眼科医生诊断。我们提取与形态学视网膜血管网络交替相关的最易变的特征。提取的七个特征与多重分形分析结果有关，这些结果描述了血管网络的结构和间隙分布。有监督的人工神经网络（ANN）将图像分为三类：正常，无糖尿病性视网膜病变的糖尿病和非增殖性DR。

结果：发现人类视网膜血管网络结构是一个分形系统。多重分形几何描述了视网膜中的不规则性和间隙分布。我们从研究的图像中提取了七个特征。特征是最大f（α）奇点谱的广义尺寸D ₀ ，D ₁ ，D ₂ ，α，谱宽，谱对称位移点和色盲性。人工神经网络获得的单值决策分类精度为97.78％，最小灵敏度为96.67％。

结论：可以使用高分辨率OCTA图像进行无创检测DR的早期阶段，该图像通过多分形几何参数化分析并通过复杂的人工神经网络实现，分类精度为96.67％。这种方法可以促进危险性分层，以决定糖尿病视网膜病变的早期诊断。