Diabetic Retinopathy (DR) may result in various degrees of vision loss and even blindness if not diagnosed in a timely manner. Therefore, having an annual eye exam helps early detection to prevent vision loss in earlier stages, especially for diabetic patients. Recent technological advances made smartphone-based retinal imaging systems available on the market to perform small-sized, low-powered, and affordable DR screening in diverse environments. However, the accuracy of DR detection depends on the field of view and image quality. Since smartphone-based retinal imaging systems have much more compact designs than a traditional fundus camera, captured images are likely to be the low quality with a smaller field of view. Our motivation in this paper is to develop an automatic DR detection model for smartphone-based retinal images using the deep learning approach with the ResNet50 network. This study first utilized the well-known AlexNet, GoogLeNet, and ResNet50 architectures, using the transfer learning approach. Second, these frameworks were retrained with retina images from several datasets including EyePACS, Messidor, IDRiD, and Messidor-2 to investigate the effect of using images from the single, cross, and multiple datasets. Third, the proposed ResNet50 model is applied to smartphone-based synthetic images to explore the DR detection accuracy of smartphone-based retinal imaging systems. Based on the vision-threatening diabetic retinopathy detection results, the proposed approach achieved a high classification accuracy of 98.6%, with a 98.2% sensitivity and a 99.1% specificity while its AUC was 0.9978 on the independent test dataset. As the main contributions, DR detection accuracy was improved using the transfer learning approach for the ResNet50 network with publicly available datasets and the effect of the field of view in smartphone-based retinal imaging was studied. Although a smaller number of images were used in the training set compared with the existing studies, considerably acceptable high accuracies for validation and testing data were obtained.

如果不及时诊断，糖尿病性视网膜病（DR）可能导致各种程度的视力丧失，甚至失明。因此，每年进行一次眼科检查有助于及早发现，以防止早期阶段的视力下降，尤其是对于糖尿病患者。最近的技术进步使基于智能手机的视网膜成像系统在市场上可用，从而可以在各种环境中执行小型，低功耗且价格合理的DR筛查。但是，DR检测的准确性取决于视场和图像质量。由于基于智能手机的视网膜成像系统比传统的眼底照相机具有更紧凑的设计，因此所捕获的图像很可能具有较低的质量，并且视野范围较小。本文的目的是使用ResNet50网络的深度学习方法为基于智能手机的视网膜图像开发自动DR检测模型。这项研究首先使用转移学习方法，利用了著名的AlexNet，GoogLeNet和ResNet50架构。其次，这些框架接受了来自多个数据集（包括EyePACS，Messidor，IDRiD和Messidor-2）的视网膜图像的重新训练，以研究使用来自单个，交叉和多个数据集的图像的效果。第三，将所提出的ResNet50模型应用于基于智能手机的合成图像，以探索基于智能手机的视网膜成像系统的DR检测精度。根据威胁视力的糖尿病视网膜病变的检测结果，提出的方法实现了98.6％的高分类准确率（98）。在独立测试数据集上，灵敏度为2％，特异性为99.1％，而AUC为0.9978。作为主要贡献，使用带有公开数据集的ResNet50网络的转移学习方法提高了DR检测的准确性，并研究了视场在基于智能手机的视网膜成像中的作用。尽管与现有研究相比，训练集中使用的图像数量较少，但是获得了相当可接受的用于验证和测试数据的高精度。