Multi-label classification (MLC) is deemed as an effective and dynamic research topic in the medical image analysis field. For ophthalmologists, MLC benefits can be utilized to detect early diabetic retinopathy (DR) signs, as well as its different grades. This paper proposes a comprehensive computer-aided diagnostic (CAD) system that exploits the MLC of DR grades using colored fundus photography. The proposed system detects and analyzes various retina pathological changes accompanying DR development. We extracted some significant features to differentiate healthy from DR cases as well as differentiate various DR grades. First, we preprocessed the retinal images to eliminate noise and enhance the image quality by using histogram equalization for brightness preservation based on dynamic stretching technique. Second, the images were segmented to extract four pathology variations, which are blood vessels, exudates, microaneurysms, and hemorrhages. Next, six various features were extracted using a gray level co-occurrence matrix, the four extracting areas, and blood-vessel bifurcation points. Finally, the features were supplied to a support vector machine (SVM) classifier to distinguish normal and different DR grades. To train and test the proposed system, we utilized four benchmark datasets (two of them are multi-label datasets) using six performance metrics. The proposed system achieved an average accuracy of $89.2\text{\%}$, sensitivity of $85.1\text{\%}$, specificity of $85.2\text{\%}$, positive predictive value of $92.8\text{\%}$, area under the curve of $85.2\text{\%}$, and Disc similarity coefficient (DSC) of $88.7\text{\%}$. The experiments show promising results as compared with other systems.

多标签分类（MLC）被认为是医学图像分析领域中有效且动态的研究主题。对于眼科医生来说，MLC的好处可用于检测早期糖尿病性视网膜病（DR）征兆以及其不同等级。本文提出了一种综合的计算机辅助诊断（CAD）系统，该系统利用彩色眼底照相技术开发DR级的MLC。拟议的系统检测并分析伴随DR发展而引起的各种视网膜病理变化。我们提取了一些重要的特征来区分健康的DR患者和不同的DR等级。首先，我们通过基于动态拉伸技术的直方图均衡化来保持亮度，对视网膜图像进行预处理以消除噪声并提高图像质量。第二，图像被分割以提取四种病理变化，分别是血管，渗出液，微动脉瘤和出血。接下来，使用灰度共生矩阵，四个提取区域和血管分叉点提取六个特征。最后，将特征提供给支持向量机（SVM）分类器，以区分正常和不同的DR等级。为了训练和测试所提出的系统，我们利用四个性能指标使用了四个基准数据集（其中两个是多标签数据集）。提出的系统的平均精度为 这些功能被提供给支持向量机（SVM）分类器，以区分正常和不同的DR等级。为了训练和测试所提出的系统，我们利用四个性能指标使用了四个基准数据集（其中两个是多标签数据集）。提出的系统的平均精度为 这些功能被提供给支持向量机（SVM）分类器，以区分正常和不同的DR等级。为了训练和测试所提出的系统，我们利用四个性能指标使用了四个基准数据集（其中两个是多标签数据集）。提出的系统的平均精度为$89.2\text{％}$，敏感度 $85.1\text{％}$，特异性 $85.2\text{％}$，正预测价值 $92.8\text{％}$，曲线下的面积 $85.2\text{％}$，以及圆盘相似系数（DSC） $88.7\text{％}$。与其他系统相比，实验显示出令人鼓舞的结果。