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ENDURING EYE CARE WITH SMARTPHONES AIDING REAL TIME DIAGNOSIS

机译:用智能手机持久的眼部护理,帮助实时诊断

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In today's world, eye defects are more common among people of all ages. Most serious disorders of the eye include retinal detachment, macular degeneration. Technology is ever emergent and improving the way that assists in analyzing fundal images. Smart phones deployed with android applications leads to promising means for significant enrichment in eye care aids. In this paper an, 'add-on' for effective detection of such eye defects is presented that can be incorporated in smartphones. The developed add-on initially acquires L*a*b* triplets of the given fundus image. The resulting L*a*b* triplets is then contrast enhanced for further fundus examination as the image is captured under non-uniform illumination environment. Subsequent steps involve feature extraction and defect classification with Artificial neural network based Back Propagation Algorithm. Performance analysis of the proposed system is evaluated using fundus images attained from DRIVE, MESSIDOR and STARE database. The ROC analysis depicts a consistent performance and 90% classification accuracy for images of different database. Hence, this application improves efficacy of retinal diagnosis and aids in timely assessment of retinal disorders. This application is developed in android platform and is compatible with existing smartphones, augmenting its features. A step by step procedure for installation and operation of the add-on in smartphones is also presented in the paper.
机译:在今天的世界中,眼睛缺陷在所有年龄段的人群中更为常见。最严重的眼睛疾病包括视网膜脱离,黄斑变性。技术曾经有兴奋,改善了有助于分析鞋面图像的方式。随着Android应用部署的智能手机导致有希望的眼部护理艾滋病富集的意义。在本文中,提出了一种用于有效检测这种眼缺陷的“加载项”,其可以包含在智能手机中。开发的附加组件最初获取给定的眼底图像的L * A * B *三胞胎。然后,由于在非均匀照明环境下捕获图像,因此得到的L * A * B *三元组对比增强,以获得进一步的眼底检查。随后的步骤涉及基于人工神经网络的后传播算法的特征提取和缺陷分类。使用从Drive,Messidor和Stare数据库获得的眼底图像进行评估所提出的系统的性能分析。 ROC分析描述了不同数据库图像的一致性和90%的分类准确性。因此,本申请改善了视网膜诊断和艾滋病的疗效及时评估视网膜障碍。此应用程序是在Android平台中开发的,与现有智能手机兼容,增强其功能。纸张还介绍了智能手机上附加组件的安装和操作的步骤。

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