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A Hybrid Classifier for the Detection of Microaneurysms in Diabetic Retinal Images

机译：一种用于检测糖尿病视网膜图像中的微肠症的混合分类器

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Diabetic Retinopathy (DR) is a chronic, progressive ocular disease in which the human retina is affected due to an increasing amount of insulin in blood. The prevalence and incidence of DR is associated with people having prolonged hyperglycaemia and other symptoms linked with diabetes mellitus. DR, if not detected and treated in time poses threat to the patient's vision ultimately causing total blindness. Among the various clinical signs, microaneurysms (MAs) appear as the early and first sign of DR. The accurate and reliable detection of microaneurysms is a challenging problem owing to its tiny size and low contrast. Successful detection of microaneurysms would be more useful for a proper planning and appropriate treatment of the disease at the early stage. The work mainly envisages the improvement of the classification accuracy by employing a hybrid classifier which combines Support Vector Machine (SVM), Naive Bayes Classifier and the decision tree. In contrast to many other classifiers the proposed classifier works efficiently, proves to be simple in terms of computational complexity and also gives good results. The performance is evaluated using publicly available retinal image database DIARETDB1.The hard decision fusion among the three classifiers carried out using the majority voting rule gives accuracy, sensitivity and specificity of 82.2916%, 82.692%, 81.818% respectively.
机译：糖尿病视网膜病变（DR）是一种慢性渐进的眼部疾病，其中人视网膜受到血液中胰岛素量增加的影响。 DR的患病率和发病率与具有延长高血糖和与糖尿病有关的其他症状的人有关。博士，如果没有检测到并随着时间的时间对待对患者的愿景造成威胁，最终导致完全失明。在各种临床症状中，MicroNeuRysms（MAS）显示为博士的早期和第一标志。由于其微小尺寸和低对比度，对微内瘤的准确和可靠的检测是一个具有挑战性的问题。成功检测在早期阶段适当的规划和适当治疗疾病的适当策划更有用。该工作主要设想通过采用混合分类器来改进分类准确性，该混合分类器组合支持向量机（SVM），天真贝叶斯分类器和决策树。与许多其他分类器相比，所提出的分类器有效地工作，证明在计算复杂性方面是简单的，并且还提供了良好的结果。使用公开的视网膜图像数据库DiaRetdB1评估性能。使用大多数投票规则进行的三分类器中的硬决策融合，可分别为82.2916％，82.692％，8118.818％的准确性，敏感性和特异性。

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