Al-Jarrah Mohammad A, Shatnawi Hadeel
a Computer Engineering Department , Yarmouk University , Irbid , Jordan.
J Med Eng Technol. 2017 Aug;41(6):498-505. doi: 10.1080/03091902.2017.1358772. Epub 2017 Aug 8.
Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection of retinopathy progress in individuals with diabetes is critical for preventing visual loss. Non-proliferative DR (NPDR) is an early stage of DR. Moreover, NPDR can be classified into mild, moderate and severe. This paper proposes a novel morphology-based algorithm for detecting retinal lesions and classifying each case. First, the proposed algorithm detects the three DR lesions, namely haemorrhages, microaneurysms and exudates. Second, we defined and extracted a set of features from detected lesions. The set of selected feature emulates what physicians looked for in classifying NPDR case. Finally, we designed an artificial neural network (ANN) classifier with three layers to classify NPDR to normal, mild, moderate and severe. Bayesian regularisation and resilient backpropagation algorithms are used to train ANN. The accuracy for the proposed classifiers based on Bayesian regularisation and resilient backpropagation algorithms are 96.6 and 89.9, respectively. The obtained results are compared with results of the recent published classifier. Our proposed classifier outperforms the best in terms of sensitivity and specificity.
在大多数国家，糖尿病视网膜病变（DR）会导致糖尿病患者在工作年龄失明。全球糖尿病患者数量不断增加，这表明DR仍将是导致视力丧失的主要原因。早期发现糖尿病患者视网膜病变的进展对于预防视力丧失至关重要。非增殖性DR（NPDR）是DR的早期阶段。此外，NPDR可分为轻度、中度和重度。本文提出了一种基于形态学的新型算法，用于检测视网膜病变并对每个病例进行分类。首先，该算法检测三种DR病变，即出血、微动脉瘤和渗出物。其次，我们从检测到的病变中定义并提取了一组特征。所选特征集模拟了医生在对NPDR病例进行分类时所寻找的特征。最后，我们设计了一个三层人工神经网络（ANN）分类器，将NPDR分为正常、轻度、中度和重度。使用贝叶斯正则化和弹性反向传播算法训练ANN。基于贝叶斯正则化和弹性反向传播算法的所提出分类器的准确率分别为96.6和89.9。将所得结果与最近发表的分类器的结果进行比较。我们提出的分类器在灵敏度和特异性方面优于最佳分类器。
