Due to the ongoing advancement in detection of critical diseases, there is a need in revamping the accurate diagnosis of diabetic retinopathy (DR). In this current study, locust based genetic classifier plays a crucial role in early screening of DR and to determine the exact location of the affected region of retina. Initially, preprocessing is performed to remove the obnoxious information such as noise present in the image and helps to transform the RGB format to gray scale image. It is done by applying wiener filter technique. After removing the obnoxious information, exudate segmentation is performed. After splitting out the image into samples, feature extraction is applied by Gabor based region covariance matrix. It helps to reduce the feature in the DIARETDB1 dataset by obtaining a new feature. After obtaining the feature, whale optimization is performed to pick out the best features such as mean, exudate area, optic distance and standard deviation and finally locust based genetic classifier is used to analogize between trained and test set data and it provides infallible information to the ophthalmologist to provide timely treatments. Comparative analysis is performed. It reveals the significant performance of the current approach over other existing SVM and CNN classifier. The results obtained from the current study shows a promising future and it achieves an accuracy of 98.9%.

由于在关键疾病检测方面的不断进步，因此需要改进对糖尿病性视网膜病（DR）的准确诊断。在这项当前的研究中，基于蝗虫的遗传分类器在DR的早期筛查以及确定视网膜受影响区域的确切位置方面起着至关重要的作用。最初，执行预处理以除去令人讨厌的信息（例如图像中存在的噪声），并有助于将RGB格式转换为灰度图像。这是通过应用维纳滤波技术完成的。去除讨厌的信息后，进行渗出液分割。将图像分割成样本后，通过基于Gabor的区域协方差矩阵进行特征提取。通过获取新特征，它有助于减少DIARETDB1数据集中的特征。获得功能后，进行鲸鱼优化以挑选出最佳特征，例如均值，渗出面积，视距和标准差，最后使用基于蝗虫的遗传分类器在训练数据和测试数据之间进行类比，并为眼科医生提供可靠的信息以提供及时的治疗。进行比较分析。它揭示了当前方法相对于其他现有SVM和CNN分类器的显着性能。从当前研究中获得的结果显示了一个有希望的未来，它可以达到98.9％的准确度。视距和标准差以及最终基于蝗虫的遗传分类器可用于在训练数据和测试数据之间进行类比，并为眼科医生提供及时的治疗信息。进行比较分析。它揭示了当前方法相对于其他现有SVM和CNN分类器的显着性能。从当前研究中获得的结果显示了一个有希望的未来，它可以达到98.9％的准确度。视距和标准差以及最终基于蝗虫的遗传分类器可用于在训练数据和测试数据之间进行类比，并为眼科医生提供及时的治疗信息。进行比较分析。它揭示了当前方法相对于其他现有SVM和CNN分类器的显着性能。从当前研究中获得的结果显示了一个有希望的未来，它可以达到98.9％的准确度。