Deep learning is an emerging trend with enormous applications over the past few years. Ophthalmology is one such area in medical applications where early disease detection is required to avoid loss of vision. Glaucoma is a rapidly growing disorder related to human eye, which arises due to the increase in pressure inside the eye. The medical diagnosis methods available for glaucoma have some limitations; hence, computer-aided design (CAD) approach is preferred using images. In the context of image processing, convolution neural networks (CNNs) are preferred for classification because of their ability to grasp highly discriminate features from raw pixel intensities. In our approach, diagnosis of glaucoma is implemented by extracting the region of interest (ROI) by splitting the coefficients into recurrence decays and will improve the possibility of identifying even poorly differentiated exudates and upgrading the normal recurrence ranges. Later, a sequential deep neural network (DNN) model with a rectified linear unit (ReLU) and sigmoid function is designed to train the data with effective features matching from training and testing samples. The proposed model is implemented on two publicly available datasets (Drishti-GS1 and ACRIMA) using 10-fold cross validation (CV), 60:40 and 70:30 split ratio approaches, and performance is assessed using the metrics and plotted the region of convergence curves. The model is also tested on two more datasets (ORIGA and Refuge) to validate the robustness of the proposed model. The obtained simulation results and the evaluated performance metrics prove that our proposed model diagnose glaucoma from retinal fundus images effectively compared with other existing models.

中文翻译：

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使用视盘定位和序列 DNN 模型从视网膜眼底图像诊断青光眼

深度学习是一种新兴趋势，在过去几年中有着巨大的应用。眼科是医疗应用领域之一，需要早期疾病检测以避免视力丧失。青光眼是一种与人眼相关的快速发展的疾病，是由于眼内压力增加而引起的。青光眼的医学诊断方法有一定的局限性；因此，首选使用图像的计算机辅助设计 (CAD) 方法。在图像处理领域，卷积神经网络 (CNN) 更适合用于分类，因为它们能够从原始像素强度中掌握高度区分的特征。在我们的方法中，青光眼的诊断是通过将系数分割为复发衰减来提取感兴趣区域（ROI）来实现的，并且将提高识别分化较差的渗出物并升级正常复发范围的可能性。随后，设计了具有修正线性单元（ReLU）和 sigmoid 函数的顺序深度神经网络（DNN）模型，通过训练和测试样本的有效特征匹配来训练数据。所提出的模型使用 10 倍交叉验证 (CV)、60:40 和 70:30 分流比方法在两个公开可用的数据集（Drishti-GS1 和 ACRIMA）上实现，并使用指标评估性能并绘制区域收敛曲线。该模型还在另外两个数据集（ORIGA 和 Refuge）上进行了测试，以验证所提出模型的稳健性。获得的模拟结果和评估的性能指标证明，与其他现有模型相比，我们提出的模型可以有效地从视网膜眼底图像诊断青光眼。