Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, India.
Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, Tamil Nadu, India.
J Med Syst. 2017 Nov 9;41(12):201. doi: 10.1007/s10916-017-0853-x.
The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection of DR screening system using Bagging Ensemble Classifier (BEC) is investigated. With the help of voting the process in ML-BEC, bagging minimizes the error due to variance of the base classifier. With the publicly available retinal image databases, our classifier is trained with 25% of RI. Results show that the ensemble classifier can achieve better classification accuracy (CA) than single classification models. Empirical experiments suggest that the machine learning-based ensemble classifier is efficient for further reducing DR classification time (CT).
糖尿病的主要并发症是糖尿病性视网膜病变（DR），这是一种视网膜血管疾病，可导致失明。定期进行早期 DR 疾病筛查被认为是一项劳动强度大且资源密集型的任务。因此，使用计算技术自动检测 DR 疾病是一个很好的解决方案。自动方法更可靠，可以确定眼底图像（FI）中是否存在异常，但分类过程的效果较差。最近，已经有一些研究工作旨在分析 FI 中的纹理判别能力，以区分健康图像。然而，由于维度较高，特征提取（FE）过程并未很好地执行。因此，为了使用机器学习和集成分类方法（称为机器学习装袋集成分类器（ML-BEC））识别用于 DR 疾病诊断和早期检测的视网膜特征，设计了 ML-BEC 方法。ML-BEC 方法包括两个阶段。在 ML-BEC 方法的第一阶段，从视网膜图像（RI）中提取候选对象。用于 DR 疾病诊断的候选对象或特征包括血管、视神经、神经组织、神经视网膜边缘、视盘大小、厚度和方差。这些特征最初是通过应用机器学习技术 t-分布随机近邻嵌入（t-SNE）提取的。此外，t-SNE 在高维图像中生成概率分布，其中图像被分为相似和不相似的对。然后，t-SNE 在低维地图上的点之间描述相似的概率分布。这减少了两个分布中关于地图上点的位置的柯尔莫哥洛夫-莱布勒散度。第二阶段包括应用集成分类器对提取的特征进行分析，以使用机器学习对数字 FI 进行准确分析。在这一阶段，研究了一种使用装袋集成分类器（BEC）的 DR 筛查系统的自动检测。在 ML-BEC 的投票过程的帮助下，装袋可以最小化由于基本分类器的方差而导致的误差。使用公开的视网膜图像数据库，我们的分类器用 25%的 RI 进行训练。结果表明，与单个分类模型相比，集成分类器可以实现更好的分类精度（CA）。实验结果表明，基于机器学习的集成分类器可以有效地进一步减少 DR 分类时间（CT）。
