The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-processing step (e.g., class balancing, feature selection) and the effects of limiting the region-of-interest to fovea on the classification performance are critically analyzed. Finally, the selection and implication of operating point on Receiver Operating Characteristic curve are discussed. The results of this study convincingly demonstrate that by following these fundamental practices of machine learning, a basic k-NN based classifier could effectively accomplish the CSME screening.

中文翻译：

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自动筛选具有临床意义的黄斑水肿的有效框架

本研究提出了一种自动筛查临床显着黄斑水肿 (CSME) 的新方法，并解决了与此类筛查相关的两个主要挑战，即渗出液分割和不平衡的数据集。所提出的方法通过将预训练的深度神经网络与元启发式特征选择相结合，取代了传统的基于渗出液分割的特征提取。正在使用特征空间过采样技术来克服偏斜数据集的影响，并且筛选是由基于 k-NN 的分类器完成的。每个数据处理步骤（例如，类平衡、特征选择）的作用以及将感兴趣区域限制为中央凹对分类性能的影响进行了批判性分析。最后，讨论了工作点的选择和对受试者工作特征曲线的影响。这项研究的结果令人信服地证明，通过遵循机器学习的这些基本实践，基于 k-NN 的基本分类器可以有效地完成 CSME 筛选。