Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than half a million diabetic patients will have diabetic retinopathy by the 22nd century. Many effective methods have been proposed for disease detection with deep learning. In this study, unlike other studies, a deep learning-based method has been proposed in which diabetic retinopathy lesions are detected automatically and independently of datasets, and the detected lesions are classified. In the first stage of the proposed method, a data pool is created by collecting diabetic retinopathy data from different datasets. With Faster RCNN, lesions are detected, and the region of interests are marked. The images obtained in the second stage are classified using the transfer learning and attention mechanism. The method tested in Kaggle and MESSIDOR datasets reached 99.1% and 100% ACC and 99.9% and 100% AUC, respectively. When the obtained results are compared with other results in the literature, it is seen that more successful results are obtained.

中文翻译：

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基于深度学习的糖尿病视网膜病变病变检测和分类的有效方法


糖尿病视网膜病变是由于糖尿病对眼睛的有害影响而发生的。糖尿病视网膜病变也是一种应该早期诊断的疾病。如果不及早治疗，可能会出现视力丧失。据估计，到 22 世纪，超过 50 万糖尿病患者中的三分之一将患有糖尿病视网膜病变。人们提出了许多有效的深度学习疾病检测方法。与其他研究不同，本研究提出了一种基于深度学习的方法，独立于数据集自动检测糖尿病视网膜病变病变，并对检测到的病变进行分类。在所提出方法的第一阶段，通过从不同数据集中收集糖尿病视网膜病变数据来创建数据池。使用 Faster RCNN，可以检测病变并标记感兴趣区域。使用迁移学习和注意力机制对第二阶段获得的图像进行分类。该方法在 Kaggle 和 MESSIDOR 数据集上测试的 ACC 分别达到 99.1% 和 100%，AUC 分别达到 99.9% 和 100%。当将所得结果与文献中的其他结果进行比较时，可以看出获得了更成功的结果。