Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, India.
J Med Syst. 2019 Jul 30;43(9):299. doi: 10.1007/s10916-019-1427-x.
Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F - measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.
青光眼是一种可能导致部分或完全视力丧失的眼部疾病。眼内压升高是导致这种疾病的主要原因。青光眼筛查和早期发现可以避免视力丧失。计算机辅助诊断（CAD）是一种自动化过程，通过对数字眼底图像进行定量分析，有可能早期发现青光眼。为 CAD 准备一个有效的模型需要一个大型数据库。本研究提出了一种基于机器学习的 CAD 工具，用于精确检测青光眼。自动编码器经过训练，可以从眼底图像中确定有效和重要的特征。这些特征用于开发青光眼测试的类别。该方法利用 1426 张数字眼底图像（589 张对照和 837 张青光眼）获得了 0.95 的 F-度量值。该系统的功效是明显的，表明它可能作为临床决策验证的附加工具使用。
