This article aims to classify diabetic retinopathy (DR) disease into five different classes using an ensemble approach based on two popular pre-trained convolutional neural networks: VGG16 and Inception V3. The proposed model aims to leverage the strengths of the two individual nets to enhance the classification performance for diabetic retinopathy. The ensemble model architecture involves freezing a portion of the layers in each pre-trained model to utilize their learned representations effectively. Global average pooling layers are added to transform the output feature maps into fixed-length vectors. These vectors are then concatenated to form a consolidated representation of the input image. The ensemble model is trained using a dataset of diabetic retinopathy images (APTOS), divided into training and validation sets. During the training process, the model learns to classify the retinal images into the corresponding diabetic retinopathy classes. Experimental results on the test set demonstrate the efficacy of the proposed ensemble model for DR classification achieving an accuracy of 96.4%.

翻译：本文旨在利用基于两种预训练卷积神经网络（VGG16和Inception V3）的集成方法，将糖尿病视网膜病变（DR）分为五类。所提出的模型旨在结合两个独立网络的优点，以提高糖尿病视网膜病变的分类性能。集成模型架构涉及冻结各个预训练模型中的部分层，以有效利用它们学到的特征表示。添加全局平均池化层，将输出的特征图转换为固定长度的向量，然后拼接这些向量以形成输入图像的整合表示。该集成模型使用糖尿病视网膜病变图像数据集（APTOS）进行训练，该数据集被分为训练集和验证集。在训练过程中，模型学习将视网膜图像分类为相应的糖尿病视网膜病变类别。在测试集上的实验结果表明，所提出的集成模型对糖尿病视网膜病变分类具有有效性，准确率达到96.4%。