Diabetic Retinopathy (DR) is considered as the complication of Diabetes Mellitus that damages the blood vessels in the retina. This is characterized as a serious vision-threatening problem in most of the diabetic subjects. Effective automatic classification of diabetic retinopathy is a challenging task in the medical field. The feature extraction plays an eminent role in the effective classification of disease. The proposed work focuses on the extraction of Haralick and Anisotropic Dual-Tree Complex Wavelet Transform (ADTCWT) features that can perform reliable DR classification from retinal fundus images. The Haralick features are based on second-order statistics and ADTCWT reliably extracts the directional features in images. The proposed work concentrates on both binary classification as well as multiclass classification of DR. The system is evaluated across various classifiers such as Support Vector Machine (SVM), Random Forest, Random Tree, J48 classifiers by giving input image features extracted from the MESSIDOR, KAGGLE and DIARETDB0 databases. The performances of the classifiers are analyzed by comparing specificity, precision, recall, False Positive Rate (FPR) and accuracy values for each classifier. The evaluation results show that by applying the proposed feature extraction method, Random Forest outperforms all the other classifiers with an average accuracy of 99.7% and 99.82% for binary and multiclass classification respectively.

中文翻译：

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使用 Haralick 和多分辨率特征对糖尿病视网膜病变进行自动二元和多类分类


糖尿病视网膜病变（DR）被认为是糖尿病的并发症，会损害视网膜血管。这在大多数糖尿病受试者中被描述为严重威胁视力的问题。糖尿病视网膜病变的有效自动分类是医学领域的一项具有挑战性的任务。特征提取在疾病的有效分类中发挥着重要作用。拟议的工作重点是提取 Haralick 和各向异性双树复小波变换 (ADTCWT) 特征，这些特征可以从视网膜眼底图像中执行可靠的 DR 分类。 Haralick 特征基于二阶统计，ADTCWT 可靠地提取图像中的方向特征。所提出的工作集中于 DR 的二元分类和多类分类。通过提供从 MESSIDOR、KAGGLE 和 DIARETDB0 数据库提取的输入图像特征，系统可以在支持向量机 (SVM)、随机森林、随机树、J48 分类器等各种分类器上进行评估。通过比较每个分类器的特异性、精确度、召回率、误报率 (FPR) 和准确度值来分析分类器的性能。评估结果表明，通过应用所提出的特征提取方法，随机森林优于所有其他分类器，二元分类和多类分类的平均准确率分别为 99.7% 和 99.82%。