In general, diabetic retinopathy is a hurdle of diabetes that subsists throughout the world. Early detection of this severe disease through computer‐assisted diagnosis tools followed by the right treatment at the right time could control its terrible condition. From the last 2 years, numerous research efforts in this area have been introduced for the automatic detection of diabetic retinopathy with appropriate evaluations. However, there is a large variability in the databases and evaluation criteria used in the literature. Accordingly, this proposal tactics to develop a new contribution to automatic detection of diabetic retinopathy based on four main stages: “(a) image pre‐processing, (b) blood vessels segmentation, (c) feature extraction and dimension reduction, and (d) diabetic retinopathy recognition”. Two steps are used for accomplishing the image pre‐processing, (a) conversion of RGB into green channel image and (b) noise removal by median filtering. Further, the pre‐processed fundus image is subjected to Iterative segmentation‐based blood vessel segmentation. For performing the precise classification of the images, there is a prerequisite to extract the relevant informative features from the segmented blood vessels. Here, the features are extracted using discrete wavelet transform, and gray‐level co‐occurrence matrix. To attain the unique features with different information, the dimension reduction process is applied using principle component analysis. Finally, the Diabetic Retinopathy recognition is performed enabling a hybrid classifier, which merges the beneficial concepts of neural network, and convolutional neural network. As the main novelty, the number of hidden neurons in both neural network and convolutional neural network is optimized by the modified rider optimization algorithm called improvement counter‐based rider optimization algorithm intending to maximize the diagnostic accuracy. Moreover, convolutional neural network takes the transformed form of the segmented blood vessels using Discrete Wavelet Transform as input, and Neural Network takes dimension reduced features as input, and AND‐bit operation of the both classified outputs provides the diagnostic results, whether the corresponding image is normal or abnormal.

中文翻译：

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用于诊断糖尿病性视网膜病变的优化混合分类器：迭代血管分割过程

通常，糖尿病性视网膜病是世界范围内仍存在的糖尿病的障碍。通过计算机辅助诊断工具尽早发现这种严重疾病，然后在正确的时间进行正确的治疗，可以控制其严重的状况。从最近的两年开始，已经在该领域进行了许多研究工作，以通过适当的评估来自动检测糖尿病性视网膜病。但是，文献中使用的数据库和评估标准存在很大的差异。因此，该提案策略基于以下四个主要阶段，为自动检测糖尿病性视网膜病变做出了新贡献：“（a）图像预处理，（b）血管分割，（c）特征提取和降维，以及（d ）糖尿病视网膜病变识别”。使用两个步骤来完成图像预处理：（a）将RGB转换为绿色通道图像，以及（b）通过中值滤波去除噪声。此外，对预处理的眼底图像进行基于迭代分割的血管分割。为了进行图像的精确分类，必须从分割的血管中提取相关的信息特征。在这里，使用离散小波变换和灰度共现矩阵提取特征。为了获得具有不同信息的独特功能，使用主成分分析应用降维过程。最后，通过混合分类器执行糖尿病性视网膜病变识别，该分类器融合了神经网络和卷积神经网络的有益概念。作为主要的创新，神经网络和卷积神经网络中隐藏神经元的数量通过改进的车手优化算法（基于改进计数器的车手优化算法）进行了优化，旨在最大程度地提高诊断准确性。此外，卷积神经网络采用离散小波变换作为输入，采用分段血管的变换形式，而神经网络则采用降维特征作为输入，两个分类输出的AND位运算提供诊断结果，无论对应的图像是正常还是异常。神经网络和卷积神经网络中隐藏神经元的数量通过改进的基于最佳计数器的骑手优化算法的改进骑手优化算法进行了优化，旨在最大程度地提高诊断准确性。此外，卷积神经网络采用离散小波变换作为输入，采用分段血管的变换形式，而神经网络则采用降维特征作为输入，两个分类输出的AND位运算提供诊断结果，无论对应的图像是正常还是异常。神经网络和卷积神经网络中隐藏神经元的数量通过改进的基于最佳计数器的骑手优化算法的改进骑手优化算法进行了优化，旨在最大程度地提高诊断准确性。此外，卷积神经网络采用离散小波变换作为输入，采用分段血管的变换形式，而神经网络则采用降维特征作为输入，两个分类输出的AND位运算提供诊断结果，无论对应的图像是正常还是异常。