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Machine Learning on Cataracts Classification Using SqueezeNet

机译:机器学习白内障的分类使用挤压胶

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Cataracts is a serious eye disease, affecting over 20 million people worldwide. It is the clouding of the lens, which blocks the light to go through the lens and project on the retina [1]. As a result, the nerve cannot transfer the whole image to the brain, leading to blindness. A vast majority of cataracts patients are people who are over 50 years old. To classify different areas of cataracts in lens, we use supervised training of convolutional neural network to train 420 images of cataracts on the lens taken from slit-lamps. The experiment can make the future of classifying cataracts more easily and ophthalmologists can apply operations to different categories of cataracts within a shorter time to cure patients with cataracts. For those people in the countryside, even not so experienced doctors can take the photo of lens and use the program to classify cataracts correctly.
机译:白内障是一种严重的眼病,影响全世界的2000多万人。它是镜头的阴影,它阻挡了光线通过镜头和突出的视网膜[1]。结果,神经不能将整个图像转移到大脑,导致失明。绝大多数白内障患者是超过50岁的人。为了对镜头中的不同区域进行分类,我们使用对卷积神经网络的监督培训来培训从狭缝灯取的镜片上的镜片420个白内障图像。实验可以使未来更容易分类白内障,眼科医生可以在较短的时间内将操作应用于不同类别的白内障,以治愈白内障患者。对于那些农村的人来说,甚至没有如此经验丰富的医生可以拍摄镜头照片并使用该程序正确分类白内障。

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