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首页> 外文会议>International Conference on Control, Electronics, Renewable Energy and Communications >Detection of Pterygium Disease Using Forward Chaining and Viola Jones Algorithm
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Detection of Pterygium Disease Using Forward Chaining and Viola Jones Algorithm

机译：使用前向链和Viola Jones算法检测翼状Disease肉疾病

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摘要

Eyesight is one of the most important senses for human life. Because only with our eyes, we can see and know the situations and conditions that occur around us. If there are problems or disorders that happen in our eyes, then we will feel uncomfortable and there are several diseases that can reduce the quality of vision and can cause blindness. In this project the author will be make an application to detect Pterygium eye disease based on the early symptoms that have been felt by the patient and find out how severely the patient affected by Pterygium disease with different levels. The stages used to determine the level of Pterygium disease is by filling all the symptoms by the patient in the application using Forward Chaining method and using an image segmentation process with Viola Jones Algorithm. The results of using the Viola Jones algorithm that have been processed using this application have an accuracy rate of 76% by testing 50 images and the results of the images detected are 38 images, in addition there are some images that are not detected and there are some images that are not detected.
机译：视力是人类生活中最重要的感觉之一。因为只有我们的眼睛，我们才能看到并了解我们周围发生的情况和状况。如果我们的眼睛发生问题或失调，那么我们会感到不舒服，并且有几种疾病会降低视力并导致失明。在该项目中，作者将根据患者感觉到的早期症状申请检出翼状eye肉眼病，并了解不同程度的患者受到翼状disease肉病的严重程度。确定翼状disease肉疾病水平的阶段是通过使用正向链结方法并使用带有Viola Jones算法的图像分割过程来填补患者在应用程序中的所有症状。通过测试50张图像，使用此应用程序处理过的Viola Jones算法的结果的准确率达到76％，检测到的图像结果为38张图像，此外，还有一些未检测到的图像，还有一些未检测到的图像。

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