# Classification of Diabetic Retinopathy Based on Efficient Computational Modeling
## 4 Experimental Section
### Comparison Experiments of Different Models  
In order to verify the classification advantages of the improved model proposed in this paper, this section compares VMamba, Mamba, VMamba-m with some locally deployed open-source classic network models in five categories. This paper uses the Ubuntu operating system and parallel computing architecture CUDA 11, the hardware platform is RTX3080 Ti 12 G (NVIDIA, Santa Clara, CA, USA), and the memory is 32 G. The models are all built using the PyTorch 2.0 deep learning framework, with the same preprocessing methods and parameter settings, and the Python version is 3.8. During training, the initial learning rate is set to 0.0001, the batch size is set to 32, and the number of iterations is 150 rounds. We measure the performance of the model based on key indicators such as accuracy, precision, recall, AUC, F1 score, and iteration time per round. After multiple rounds of experiments, the experimental results of each model on the APTOS2019 dataset are shown in Table 2. Combined with the data in the table, we can see that the Mamba series models perform well in the task of classifying diabetic retinopathy. In the five-classification task, the model of VMamba-m proposed in this paper performs the best, followed by VMamba, with classification accuracies of 0.791 and 0.714, and iterations time per round of 34.027 and 76.322, respectively, which are higher than the other models. The accuracy of training and validation is shown in Figure 5. We will increase the number of iterations in future experiments to obtain a more stable model. Similarly, the loss of validation is depicted in Figure 6.  
The ROC curve plot of our VMamba-m model is shown in Figure 7. The ROC curve and AUC value indicate how close the prediction is to the perfect classification, which is shown in the upper left corner of the ROC coordinates. The AUC value shows the area under the ROC curve. The closer the value is to 1, the better the model performance. The figure shows AUC values of 99.1%, 85.5%, 87.50%, 87%, and 86.8%, representing the categories of no DR, mild, moderate, severe, and proliferative DR, respectively. Since the morphological changes in the fundus images of moderate DR affect the identification of pathological structures, the AUC of moderate DR is lower than that of other DR, at 85.5%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **AUC** & **F1** & **Time** \\ \hline InceptionV3 & 0.701 & 0.684 & 0.618 & 0.813 & 0.650 & 106.345 \\ \hline VGG16 & 0.686 & 0.795 & 0.653 & 0.805 & 0.701 & 108.012 \\ \hline ResNet50 & 0.698 & 0.542 & 0.701 & 0.847 & 0.660 & 107.453 \\ \hline ResNet34 & 0.688 & 0.540 & 0.700 & 0.839 & 0.681 & 108.753 \\ \hline VGGNet & 0.702 & 0.550 & 0.655 & 0.832 & 0.651 & 105.424 \\ \hline DensNet121 & 0.684 & 0.574 & 0.640 & 0.814 & 0.606 & 111.652 \\ \hline ResNet108 & 0.675 & 0.702 & 0.690 & 0.821 & 0.686 & 107.379 \\ \hline NASNetMobile & 0.795 & 0.648 & 0.712 & 0.871 & 0.732 & 152.613 \\ \hline MobileNet & 0.769 & 0.655 & 0.722 & 0.874 & 0.751 & 125.410 \\ \hline MobileNetV2 & 0.759 & 0.647 & 0.724 & 0.863 & 0.756 & 130.031 \\ \hline MobileNetV3 Small & 0.692 & 0.643 & 0.694 & 0.812 & 0.701 & 127.721 \\ \hline MobileNetV3 Large & 0.665 & 0.613 & 0.684 & 0.846 & 0.717 & 129.285 \\ \hline VMamba & 0.714 & 0.500 & 0.718 & 0.881 & 0.500 & 76.322 \\ \hline Mamba & 0.703 & 0.512 & 0.706 & 0.850 & 0.501 & 77.034 \\ \hline VMamba-m & 0.791 & 0.537 & 0.766 & 0.891 & 0.527 & 34.027 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of different jobs for five classifications under Kaggle dataset. Figure 5: The accuracy plot of training and validation data. Figure 6: The loss plot of validation data. Figure 7: ROC curve of diabetic retinopathy severity grade based on VMamba-m model. Figure 8 depicts the confusion matrix, which represents how to evaluate the performance of each class in the model. The proportion of examples successfully classified for each class is shown in the diagonal cells.