## 4 Discussion  
In this study, we examined and compared the performance of eight pre-trained DNNs in DR detection. The best networks in this context were GoogleNet and ResNet-50, as they both achieved promising results across the selected criteria. Both the qualitative and quantitative results obtained in our research were in agreement, and all networks showed performance scores above 87% in detecting DR, with the highest being 100%. Although the studies of [PERSON] et al. and [PERSON] et al. dealt with the classification of retinal images, they used different databases, namely the KKU Eye Center database [11] and the DIARETDB1 database [12]. Our research focused on the APTOS 2019 dataset, which presents challenges due to the limited number of images included. [PERSON] et al. [11] and [PERSON] et al. [12] focused on the detection and classification of DR with a single algorithm, which was beneficial in some aspects but also resulted in lower overall accuracy, which was not the case for the algorithms used in our framework--specifically, for GoogleNet and ResNet-50. Moreover, according to [PERSON] et al., the sensitivity of the PNN, SVM, and SVMG models was 90, 98, and 99%, respectively, [12]. However, in our study, the sensitivity of GoogleNet was almost 100%, and that of the other networks was above 97%. This indicates the significant contributions of our framework, which mainly relies on DNNs for the automatic detection of DR. Furthermore, a thorough review of the previous research and the statistical results achieved was employed to enable the comparison with our findings. The proposed algorithm was compared to 11 methods reported in the literature, as shown in Table 7. These 11 methods were (CNN, SVM) from 2021 [28], the feedforward backpropagation NN [28], the SVM from 2021 [28], the SVM modified in 2021 for OCTA images [28], (CNN, SVM) for OCT images [36], the SVM from 2018 [12], SVMG from 2018 [12], a PNN for fundus images [12], K-Nearest Neighbors (KNN) for OCT images, random forest for OCT images [36], and a random tree method from 2020 [36]. According to the results shown in Table 7, state-of-the-art algorithms that have been presented in the literature showed superior results compared to our framework using the eight different DL networks. Although the SVM, SVMGA, and PNN models used by [PERSON] et al. showed robust results, indicated in bold, they could not be used for a rigorous comparison, as they only made use of 64 DR images. However, in our framework, we utilized a large database of 3662 images, with 1857 DR images. An additional advantage of this framework is that, regardless of the type of input image, the algorithm can identify it as a healthy or DR case automatically, with high percentages of sensitivity, accuracy, and specificity compared to the techniques available in the literature. Some evaluation measures were lacking, denoted by '-' in Table 7, due to the lack of data in [13, 28]. It is noteworthy that other recent studies, such as one published in 2022, employed UNETs, which are advanced algorithms applied to biomedical images. However, these UNETs showed a prediction score of 88% for the disease [38], and they were used for the detection of blood diseases rather than eye diseases. Although UNETs exhibit powerful prediction capabilities, our framework demonstrates stronger performance, both quantitatively and qualitatively, as reported in Table 7.  
Concerning the accuracy in DR detection relative to the number of epochs, among the employed DL networks, the accuracy ranged from 92% to 94% after epoch 2. This indicates remarkable performance as compared to the values obtained in the literature, such as in the work of [PERSON] et al. [12]. In addition, our results were based on a larger database of fundus images. With regard to the error/loss in DR detection, the new framework employing the eight DL networks exhibited a negligible error rate of less than or equal to 0.5%.