The most effective treatment for diabetic retinopathy (DR) is the early detection through regular screening, which is critical for a better prognosis. Automatic screening of the images would assist the physicians in diagnosing the condition of patients easily and accurately. This condition searches out for special importance of image processing technology in the way of processing the retinal fundus images. Accordingly, this article plans to develop an automatic DR detection model with the aid of three main stages like (a) image preprocessing, (b) blood vessel segmentation, and (c) classification. The preprocessing phase includes two steps: conversion of RGB to Lab, and contrast enhancement. The Histogram equalization process is done using the contrast enhancement of an image. To the next of preprocessing, the segmentation phase starts with a valuable procedure. It includes (a), thresholding the contrast‐enhanced and filtered images, (b) thresholding the keypoints of contrast‐enhanced and filtered images, and (c) adding both thresholded binary images. Here, the filtering process is performed by proposed adaptive average filtering, where the filter coefficients are tuned or optimized by an improved meta‐heuristic algorithm called fitness probability‐based CSO (FP‐CSO). Finally, the classification part uses Deep CNN, where the improvement is exploited on the convolutional layer, which is optimized by the same improved FP‐CSO. Since the conventional CSO depends on a fitness probability in the improved algorithm, the proposed algorithm termed as FP‐CSO. Finally, valuable comparative and performance analysis has confirmed the effectiveness of the proposed model.

中文翻译：

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优化深度卷积神经网络自适应平均滤波自动诊断糖尿病视网膜病变

糖尿病视网膜病变 (DR) 最有效的治疗方法是通过定期筛查及早发现，这对于更好的预后至关重要。图像的自动筛选将帮助医生轻松准确地诊断患者的病情。这种情况探明了图像处理技术在处理视网膜眼底图像的方式中的特殊重要性。因此，本文计划借助 (a) 图像预处理、(b) 血管分割和 (c) 分类等三个主要阶段来开发自动 DR 检测模型。预处理阶段包括两个步骤：RGB 到 Lab 的转换和对比度增强。直方图均衡过程是使用图像的对比度增强完成的。到下一步的预处理，分割阶段从一个有价值的程序开始。它包括（a）对对比度增强和过滤后的图像进行阈值处理，（b）对对比度增强和过滤后的图像的关键点进行阈值处理，以及（c）将两个阈值化的二值图像相加。在这里，滤波过程是通过提出的自适应平均滤波来执行的，其中滤波器系数通过一种称为基于适应度概率的 CSO (FP-CSO) 的改进元启发式算法进行调整或优化。最后，分类部分使用 Deep CNN，在卷积层上进行改进，并通过相同的改进 FP-CSO 进行优化。由于传统的 CSO 依赖于改进算法中的适应度概率，所提出的算法称为 FP-CSO。最后，