Glaucoma disease is affecting a large community worldwide. It gradually affects the optic nerve and may cause partial or complete vision loss. Glaucoma happens due to an increase in the fluid pressure inside the optic nerve, which is also known as intraocular pressure (IOP). Therefore, it is essential to detect it in the early stage to prevent blindness. Recently, deep neural networks have been applied to analyse medical imagery. This paper proposes a framework for glaucoma detection using the deep convolution neural network. In this framework, a preprocessing step uses the CLAHE to enhance the local contrast. Further, we have utilized two segmentation models (EfficientNet + U-Net) for segmenting the optic cup and disc mask from retinal fundus images. Moreover, the CDR ratio is computed from the segmented optic cup and disc masks. The framework detects whether the inputted image is glaucoma infected or not based on the CDR ratio. The accuracy of the proposed framework is compared to various baseline models. A qualitative and quantitative assessment has been done on various benchmark datasets (DRISHTI-GS1 and RIM-ONE). The experimental outcomes illustrate that the proposed framework outperformed the other state-of-the-art methods for glaucoma detection in the retinal fundus image.

青光眼疾病正在影响全世界的一个大社区。它会逐渐影响视神经，并可能导致部分或全部视力丧失。青光眼的发生是由于视神经内部的液体压力增加，也称为眼内压（IOP）。因此，必须在早期进行检测以防止失明。最近，深度神经网络已被用于分析医学图像。本文提出了一种使用深度卷积神经网络的青光眼检测框架。在此框架中，预处理步骤使用CLAHE增强局部对比度。此外，我们利用了两个分割模型（EfficientNet + U-Net）从视网膜眼底图像中分割视杯和椎间盘。此外，CDR比率是从分段的光学杯和盘片掩模计算得出的。框架基于CDR比率检测输入的图像是否被青光眼感染。将所提出框架的准确性与各种基准模型进行比较。已经对各种基准数据集（DRISHTI-GS1和RIM-ONE）进行了定性和定量评估。实验结果表明，所提出的框架优于视网膜眼底图像中青光眼检测的其他最新方法。