Early stage of diagnosis of eye diseases through automatic analysis in the retinal image is the emerging technology in the area of retinopathy. Glaucoma is the primary reason for the loss of visibility in people around the world. The separation of the disc and the cup in the optic region is the technique used to identify glaucoma in the human retinal image. In this paper, superpixel segmentation, followed by Modified Kernel Fuzzy C‐Means (MKFCM) algorithm is used to segment the optic disc and optic cup. The proposed segmentation method achieves a maximum average of F‐score as 0.979, an average boundary distance as 10.016 pixels, and an average correlation coefficient of 0.949. To train convolutional neural networks (CNN), the segmented images obtained by the MKFCM segmentation algorithm is given as the input for the identification of glaucoma. This CNN uses the gray level co‐occurrence matrix features calculated from the segmented image. The experiment used for this study demonstrates that CNN gives superior categorization correctness and requires fewer figures of knowledge iterations than the original CNN. The accuracy obtained by this proposed method is 94.2%. The model will help to identify the proper class of severity of glaucoma in retinal images.

中文翻译：

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具有深度学习框架的自动分割算法，用于青光眼的早期检测

通过视网膜图像自动分析来诊断眼睛疾病的早期阶段是视网膜病领域中的新兴技术。青光眼是世界各地人们看不见的主要原因。视神经区域中椎间盘和杯的分离是用于识别人视网膜图像中的青光眼的技术。在本文中，使用超像素分割，然后使用改进的核模糊C均值（MKFCM）算法对视盘和视杯进行分割。所提出的分割方法实现F分数的最大平均值为0.979，平均边界距离为10.016像素，平均相关系数为0.949。为了训练卷积神经网络（CNN），将通过MKFCM分割算法获得的分割图像作为识别青光眼的输入。此CNN使用从分割图像计算出的灰度共现矩阵特征。用于本研究的实验表明，与原始CNN相比，CNN具有更好的分类正确性，并且所需的知识迭代图更少。通过该方法获得的准确度为94.2％。该模型将有助于识别视网膜图像中青光眼严重程度的适当类别。