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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Automated detection of retinal health using PHOG and SURF features extracted from fundus images
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Automated detection of retinal health using PHOG and SURF features extracted from fundus images

机译：使用Phog和Surf功能从眼底图像自动检测视网膜健康

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摘要

Many health-related problems arise with aging. One of the diseases that is prevalent among the elderly is the loss of sight. Various eye diseases, namely age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are the prime causes of vision loss as we grow old. Nevertheless, early detection of such eye diseases can impede the progression of this problem. Therefore, the elderly are encouraged to attend regular eye checkups for early detection of eye diseases. However, it is time-consuming and laborious to conduct a mass eye screening session frequently. Hence, we proposed a novel approach to develop an automated retinal health screening system in this work. This paper discusses a retinal screening system to automatically differentiate normal image from abnormal (AMD, DR, and glaucoma) fundus images. The fundus images are subjected to the pyramid histogram of oriented gradients (PHOG) and speeded up robust features (SURF) techniques. Then, the extracted data are subjected to adaptive synthetic sampling to balance the number of data in the two classes (normal and abnormal). Subsequently, we employed the canonical correlation analysis approach to fuse the highly-correlated features extracted from the two (PHOG and SURF) descriptors. We have achieved 96.21% accuracy, 95.00% sensitivity, and 97.42% specificity with ten-fold cross-validation strategy using k-nearest neighbor (kNN) classifier. This novel algorithm has high potential in the diagnosis of normal eyes during the mass eye screening session or in polyclinics quickly and reliably. Hence, the patients having abnormal eyes can be sent to the main hospitals which will reduce the workload for the ophthalmologists. Graphical Abstract Proposed system.
机译：衰老出现了许多与健康相关的问题。在老年人中普遍存在的疾病之一是丧失视力。各种眼病，即年龄相关的黄斑变性（AMD），糖尿病视网膜病变（DR）和青光眼是我们变老的视力损失的主要原因。然而，早期发现这种眼病可能会妨碍这个问题的进展。因此，鼓励老年人参加常规眼睛检查以进行早期检测眼病。然而，经常进行大规模眼筛查会话是耗时和费力的。因此，我们提出了一种在这项工作中开发自动视网膜健康筛查系统的新方法。本文讨论了视网膜筛查系统，以自动区分正常图像（AMD，DR和Glaucoma）眼底图像。对眼底图像经受导向梯度（PHOG）的金字塔直方图，并加速鲁棒特征（冲浪）技术。然后，对提取的数据进行自适应合成采样，以平衡两个类中的数据数（正常和异常）。随后，我们采用了规范相关分析方法来融合从两个（手册和冲浪）描述符提取的高度相关特征。我们已经实现了96.21％的精度，灵敏度为95.00％，具有97.42％的特异性，使用k最近邻（knn）分类器具有十倍的交叉验证策略。这种新型算法在肿瘤筛选期间或微屏蔽中诊断常见的诊断潜力很快，可快速可靠地诊断。因此，患有异常眼睛的患者可以送到主要医院，这将减少眼科医生的工作量。图形摘要提出系统。

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