Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.

中文翻译：

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使用优化的深度卷积神经网络自动筛查糖尿病视网膜病变：增强的蛾火焰模型

如今，对视网膜图像的分析作为具有挑战性的研究领域之一存在。通过分析视网膜中发生的变化，可以识别出许多视网膜疾病。然而，这些研究的主要缺点是，它们没有更高的识别准确率。所提出的框架包括四个阶段，即（i）血管分割（ii）特征提取（iii）最佳特征选择和（iv）分类。最初，对输入的眼底图像进行血管分割，从中获取两个二进制阈值图像（一个来自高通滤波器 (HPF)，另一个来自顶帽重建）。将这两个图像区分开来，将两者共有的区域称为主要血管，将剩余区域融合形成血管子图像。这些血管子图像使用高斯混合模型（GMM）分类器进行分类，并将结果与​​主要血管相加，形成分割的血管。对分割后的图像进行特征提取处理，提取局部二值模式（LBP）、灰度共生矩阵（GLCM）和灰度游程矩阵（GLRM）等特征。由于维度灾难似乎是最大的问题，因此从提取的特征中选择合适的特征进行分类非常重要。在本文中，引入了一种新的改进的优化算法Moth Flame with New Distance Formulas (MF-NDF)，用于选择最优特征。最后，对选择的最优特征进行深度卷积神经网络（DCNN）模型进行分类。进一步，为了做出准确的诊断，DCNN的权重通过相同的优化算法进行了优化调整。所提出算法的性能将在正面和负面措施方面与传统算法进行比较。