## 3 Experimental Results  
The results obtained by applying the novel framework to the experimental optical images referred to a total of eight DNNs pre-trained on MATLAB v2021. The quantitative results regarding the performance of the eight pre-trained DNN networks are shown in Table 6.  
The quantitative results regarding DR detection are shown in Table 6. GoogleNet exhibited a remarkable sensitivity of 99.44% for DR detection. Although the sensitivity of GoogleNet surpassed that of the other seven networks--AlexNet, ResNet-50, DarkNet-53, EfficientNet-60, SqueezeNet, Inception-v3, and MobileNet-v2 other networks in DR detection, as the specificity and precision were 1.38% and 1.74% higher, respectively, than that obtained by GoogleNet. Meanwhile, their detection accuracies were the same. On the other hand, among the considered networks, MobileNet-v2 exhibited the lowest sensitivity and AlexNet showed the lowest specificity, accuracy, and precision. It is noteworthy that the overall performance of the eight networks in DR detection was acceptable in terms of all measured aspects, i.e., all measurements were above 87%, as shown in Table 6.  
Additional qualitative results regarding DR detection were obtained in the form of the ROC curves for GoogleNet, AlexNet, ResNet-50 DarkNet-53, EfficientNet-60, SqueezeNet, Inception-v3, and MobileNet-v2, as shown in Figures 7 and 8. The ROC curves of AlexNet, GoogleNet, ResNet-50, and DarkNet-53 are shown in Figures 7a, 7b, 7c and 7d, respectively. Moreover, the ROC curves of EfficientNet-60, SqueezeNet, Inception-v3, and MobileNet-v2 are shown in Figures 8a, 8b, 8c and 8d, respectively. Among the tested networks, the sensitivity in DR detection was the highest for GoogleNet and ResNet-50; therefore, the ROC curves of both GoogleNet and ResNet-50 were the best compared to the other networks. Moreover, SqueezeNet showed the second-best performance in DR detection. However, among the explored networks, MobileNet-v2 showed the weakest performance in terms of the ROC. Finally, the general behavior of the ROC curves was acceptable regarding DR detection. In addition to the ROC curves obtained to demonstrate the performance of the DL frameworks in DR detection, graphs showing the accuracy versus the number of epochs, as well as the error rate versus the epochs during training (smoothed) and validation, were obtained. These are shown in Figures 9 and 10, respectively. The accuracy of AlexNet, GoogleNet, ResNet-50, and DarkNet-53 is shown in Figure 9a-d. Their values were high and fluctuated within an approximate range of 92% to 94%. The values for these four networks converged after epoch 3; however, more fluctuations were observed for ResNet after epoch 3 compared to AlexNet, GoogleNet, and DarkNet. Furthermore, the accuracy of EfficientNet-60, SqueezeNet, Inception-v3, and MobileNet-v2, as shown in Figure 10a-d, was high and fluctuated within an approximate range of 92% to 93.5%. These values converged after epoch 2 for the networks EfficientNet-60, SqueezeNet, and MobileNet-v2; however, for Inception-v3, they converged after epoch 3. Moreover, more fluctuations were observed for EfficientNet-60, Inception-v3, and MobileNet-v2 between the training and validation data as compared to SqueezeNet. Figure 8: The Receiver Operating Characteristics (ROC) curves of the deep neural network (DNN) models: (**a**) EfficientNet-60; (**b**) SqueezeNet; (**c**) Inception-v3; and (**d**) MobileNet-v2. The error rates in DR detection using AlexNet, GoogleNet, ResNet-50, and DarkNet-53 were low, as shown in Figure 9a-d, (ranging between 0 and 0.5%) and fluctuating at around 0%. These values converged after 250 iterations for these four networks. Similarly, regarding the errors in DR detection, as shown in Figure 10a-d, the rate was less than or equal to 0.5%. These values converged after 250 iterations for these four networks.