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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Exploiting ensemble learning for automatic cataract detection and grading
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Exploiting ensemble learning for automatic cataract detection and grading

机译：利用集成学习进行自动白内障检测和分级

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摘要

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
机译：白内障定义为通常表现为视力较差的晶状体混浊。它是全世界视力障碍的最常见原因之一。早期诊断需要训练有素的医疗保健专业人员的专业知识，由于潜在的成本，这可能对早期干预构成障碍。迄今为止，文献中报道的研究利用单一学习模型对白内障严重程度进行分级的视网膜图像分类。我们提出一种基于整体学习的方法，作为提高诊断准确性的一种手段。从每个眼底图像中提取三个独立的特征集，即基于小波，草图和纹理的特征。对于每个功能集，建立两个基本学习模型，即支持向量机和反向传播神经网络。然后，研究了集成投票的方法，即多数投票和堆叠，以将多个基础学习模型结合起来，用于最终眼底图像分类。对白内障检测（两类任务，即白内障或非白内障）和白内障分级（四类任务，即非白内障，轻度，中度或重度）任务进行了经验性实验。就白内障检测和分级任务的正确分类率而言，集成分类器的最佳性能分别为93.2％和84.5％。结果表明，集成分类器明显优于单一学习模型，这也说明了该方法的有效性。 （C）2015 Elsevier Ireland Ltd.保留所有权利。

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