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Glaucoma Detection with Fully Convolutional Neural Network using Optic Disc and Segmentation Methods

机译:使用光盘和分割方法与完全卷积神经网络的青光眼检测

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The disease which affects the optic nerve of an eye and also it leads to the cause of irreversible blindness globally named glaucoma. A vertical CDR of the fundus eye image is an important clinical indicator for glaucoma diagnosis. Optic Nerve Head (ONH) assessment method plays the major role for detecting this kind of CDR used for ONH evaluation. It mainly focuses on noise removal process. Preprocessing of images is done for enhancing the contrast between cup and disk. For accurate measurement, the segmentation has been done automatically for separating the cup and disk. The Fully Convolutional Network (FCN) is employed as a main core in this deep neural network architecture. The transformation of fully connected layer into Convolutional layer is obtained by FCN and also the classification map of the image which has been sent of same size is done using upsampling.
机译:这种疾病,影响眼神的视神经,它也导致全球异常名为青光眼的不可逆失明的原因。 眼底图像的垂直CDR是青光眼诊断的重要临床指标。 视神经头(ONH)评估方法起到检测用于ONH评估的这种CDR的主要作用。 它主要专注于噪音清除过程。 采取图像的预处理,用于增强杯子和磁盘之间的对比度。 为了精确测量,分段自动完成用于分离杯子和磁盘。 完全卷积的网络(FCN)被用作该深度神经网络架构中的主要核心。 通过FCN获得完全连接层进入卷积层的转换,并且使用ups采样来完成已经发送了相同尺寸的图像的分类图。

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