## 4 Discussion
The eight networks--AlexNet, GoogleNet, ResNet-50, DarkNet-53, EfficientNet-60, SqueezeNet, Inception-v3, and MobileNet-v2--converged after 250 iterations and after epoch 2. The error rates during diagnosis were calculated and reported for the first time for these DNNs; these are shown in Table 7 and were used to link the findings obtained with the new framework to the clinical findings. An additional comparison was carried out using the methods for the diagnosis of retinal diseases. Traditional DL models, such as ResNet and GoogleNet, are often preferred over newer classification networks, such as Vision Transformer (ViT) [39], Swin Transformer (ST) [40], ConvNeXt, and Vision Mamba. This comparison is shown in Table 8, and it is based on different criteria--namely, the computational efficiency, training data requirements, feature extraction, generalization to small datasets, interpretability, availability of pre-trained medical models, localization of retinal features, stability and robustness, and adoption in medical AI applications considering CNN models [41] and ViT and ST [39, 40]. [PERSON] introduced ViT and discussed its high data dependency, as reported in Table 8. [PERSON] et al. demonstrated the performance of ST but also revealed its computational complexity [40]. On the other hand, the advantages and criteria of the CNNs were provided, for instance, their capability in detecting small abnormalities in retinal images, and their ability to be fine-tuned on medical data. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Evaluation Algorithm for DR Diagnosis** & **Type and Number of Images (N)** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** & **DR Diagnosis Error (\%)** \\ \hline
**Framework**_GoogleNet_ & Fundus images (1857) & **99.40** & 88.36 & **93.56** & **0.50\%** \\ \hline
**Framework**_ResNet-50_ & Fundus images (1857) & **97.58** & **89.74** & **93.56** & **0.25\%** \\ \hline Framework**_DurkNet-53_ & Fundus images (1857) & **98.05** & 87.56 & **92.51** & **0.25\%** \\ \hline Framework**_EfficientNet-60_ & Fundus images (1857) & **98.05** & 87.64 & **92.78** & **0.30\%** \\ \hline Framework**_SqueezeNet_ & Fundus images (1857) & **99.16** & 88.33 & **93.43** & **0.25\%** \\ \hline Framework**_Inception-v3_ & Fundus images (1857) & **97.82** & **88.86** & 93.17 & **0.25\%** \\ \hline Framework**_MobileNet-v2_ & Fundus images (1857) & **96.99** & 87.87 & **92.25** & **0.25\%** \\ \hline Framework**_AlexNet_ & Fundus images (1857) & **97.7** & 87.09 & **92.12** & **0.25\%** \\ \hline (CNN, SVM) in 2021 [28] & OCTA images (45) & 88.88 & **95.55** & - & - \\ \hline Feedforward Backpropagation NN [28] & OCTA images (45) & 66.66 & 71.11 & 68.88 & - \\ \hline SVM in 2021 [28] & OCTA images (45) & 40.00 & 62.22 & - & - \\ \hline SVM modified in 2021 [28] & OCTA images (45) & 62.22 & 73.33 & - & - \\ \hline (CNN, SVM) in 2020 [36] & OCT images (26) & - & 88 & **94** & - \\ \hline SVM in 2018 [12] & Fundus images (64) & **98** & **96** & **97.6** & - \\ \hline SVMGA in 2018 [12] & Fundus images (64) & **99** & **96** & **98.4** & - \\ \hline PNN in 2018 [12] & Fundus images (64) & **90** & 88 & 89.6 & - \\ \hline K-Nearest Neighbors (KNN) in 2020 [36] & OCT images (26) & - & 83 & 84 & - \\ \hline Random Forest in 2020 [36] & OCT images (26) & - & 82 & 82 & - \\ \hline Random Tree in 2020 [36] & OCT images (26) & - & 81 & 81 & - \\ \hline \hline \end{tabular} Note: Bold values indicate the best performance among the compared methods.  
\end{table} 
Table 7: Comparison of the statistical evaluation results regarding the detection and diagnosis of DR—namely, the sensitivity, specificity, accuracy, and precision—obtained with the new framework versus the methods described in the literature, based on the optical images involved.