论文标题
比较用于分类出血的常规和深度特征模型
Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages
论文作者
论文摘要
糖尿病性视网膜病是一种与眼睛有关的病理学，会产生异常并引起视觉障碍，适当的治疗需要识别不规则。该研究使用出血检测方法，并比较传统和深度特征的分类。特别是，方法识别与血管相关的出血或居住在视网膜边界并报告具有挑战性。最初，自适应亮度调节和对比度增强纠正了降级图像。出血的前瞻性位置是通过高斯匹配的过滤器，熵阈值和形态操作来估计的。出血是通过基于强度区域差异的新技术进行分割的。然后，通过常规方法和训练支持向量机的深层模型提取特征，并评估了结果。每个模型的评估指标都是有希望的，但是发现相对，深层模型比常规特征更有效。
Diabetic retinopathy is an eye-related pathology creating abnormalities and causing visual impairment, proper treatment of which requires identifying irregularities. This research uses a hemorrhage detection method and compares classification of conventional and deep features. Especially, method identifies hemorrhage connected with blood vessels or reside at retinal border and reported challenging. Initially, adaptive brightness adjustment and contrast enhancement rectify degraded images. Prospective locations of hemorrhages are estimated by a Gaussian matched filter, entropy thresholding, and morphological operation. Hemorrhages are segmented by a novel technique based on regional variance of intensities. Features are then extracted by conventional methods and deep models for training support vector machines, and results evaluated. Evaluation metrics for each model are promising, but findings suggest that comparatively, deep models are more effective than conventional features.
