Diabetic retinopathy (DR) is one of the complications of diabetes and a leading cause of blindness in the world. The tiny blood vessels inside the retina are damaged due to diabetes and result in various vision-related problems and it may lead to complete vision loss without early detection and treatment. Diabetic retinopathy may not cause any symptoms during its earlier stage of the disease and many physical tests such as visual acuity tests, pupil dilation, etc., are required to detect diabetic retinopathy disease. So, early detection of diabetic retinopathy disease is required to avoid vision loss. This work aims to automate the detection and grading of non-proliferative Diabetic Retinopathy from retinal fundus images using Convolution Neural Networks. The model was tested on two popular datasets such as MESSIDOR and IDRiD. Before applying the Convolution Neural Network (CNN) layers, the images were pre-processed and resolution was adjusted (256 × 256). The maximum accuracy achieved is 90.89% using MESSIDOR images. The research can be carried forward by applying various preprocessing techniques before putting them through different computational layers.

糖尿病性视网膜病（DR）是糖尿病的并发症之一，也是世界上失明的主要原因。视网膜内部的细小血管由于糖尿病而受损，并导致各种与视觉有关的问题，并且可能导致完全的视力丧失，而无需及早发现和治疗。糖尿病性视网膜病在疾病的早期阶段可能不会引起任何症状，检测糖尿病性视网膜病疾病需要许多物理检查，例如视力检查，瞳孔散大等。因此，需要及早发现糖尿病性视网膜病，以避免视力下降。这项工作旨在使用卷积神经网络从视网膜眼底图像中自动检测非增生性糖尿病性视网膜病变并对其进行分级。该模型在两个流行的数据集（如MESSIDOR和IDRiD）上进行了测试。在应用卷积神经网络（CNN）层之前，对图像进行预处理并调整分辨率（256×256）。使用MESSIDOR图像可获得的最大精度为90.89％。通过将各种预处理技术应用到不同的计算层之前，可以继续进行研究。