Diabetic retinopathy (DR) accounts in eye‐related disorders due to accumulated damage to small retinal blood vessels. Automated diagnostic systems are effective in early detection and diagnosis of severe eye complications by assisting the ophthalmologists. Deep learning‐based techniques have emerged as an advancement over conventional techniques based on hand‐crafted features. The authors have proposed a Quadrant‐based automated DR grading system in this work using Inception‐V3 deep neural network to extract small lesions present in retinal fundus images. The grading efficiency of the proposed architecture is improved utilizing image enhancement and optical disc removal pipeline along with data augmentation stage. The proposed system yields accuracy of 93.33% with minimized cross‐entropy loss of 0.291. Capability of proposed system is demonstrated experimentally to provide efficient DR diagnosis. The diagnosis ability of the proposed architecture is demonstrated by state‐of‐the‐art comparison with other mainstream convolution neural network models and a maximum improvement of 14.33% is observed.

中文翻译：

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使用基于象限的Inception-V3卷积神经网络架构对糖尿病性视网膜病变严重程度进行分级

糖尿病性视网膜病（DR）是由于视网膜小血管累积损伤引起的与眼有关的疾病。自动化的诊断系统可通过协助眼科医生来有效地早期检测和诊断严重的眼部并发症。基于深度学习的技术作为基于手工特征的传统技术的发展而出现。作者在这项工作中提出了一个基于象限的自动DR分级系统，该系统使用Inception-V3深层神经网络提取视网膜眼底图像中存在的小病变。利用图像增强和光盘删除流水线以及数据增强阶段，可以提高所提出体系结构的分级效率。拟议的系统产生93.33％的精度，最小交叉熵损失为0.291。实验证明了所提出系统的功能可提供有效的DR诊断。通过与其他主流卷积神经网络模型进行的最新技术比较，证明了所提出体系结构的诊断能力，并且观察到最大改善率为14.33％。