## 6 Discussion
### Architectures Used  
In the reviewed articles, many state-of-the-art architectures were used to classify DR. Among them, InceptionV3 was the most commonly used, followed by the AlexNet and VGG16 architectures. The choice of the architectures did not depend on the size of the dataset. In studies [55; 56; 58; 59; 61; 62; 63; 68; 69; 70], the authors compared different architectures to determine the best performing one. In studies [56; 59; 61; 63; 70], the authors compared InceptionV3 architecture to other networks, and InceptionV3 achieved the best performance in all the studies except for [63]. The lowest performance was achieved by the AlexNet architecture in the following studies: [58; 59; 61; 62; 63]. The high performance of InceptionV3 may be attributed to the inception module used. This module can capture different aspect ratios in the same image, which was shown to be very useful in DR images. The low performance of AlexNet could have been caused by the fact that it only uses five convolution layers. This number is not sufficient to accurately classify challenging images, like DR. A summary of the architectures used is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Architecture** & **Number of Classes** & **Dataset** & **Dataset Size** & **Performance Measure** & **Results** \\ \hline [PERSON] et al. [60] & AlexNet & 2 classes & Messider & 580 & Accuracy & 88.3\% \\ \hline \multirow{4}{*}{[PERSON] et al. [61]} & AlexNet & - & Kaggle & 1050 & Accuracy & 79\% \\  & VCG16 & - & e-epthma & 274 & - & 90\% \\  & GoogLeNet & 2 classes & - & - & - & 98\% \\  & ResNet & - & - & - & - & 95\% \\  & InceptionV3 & - & - & - & - & 98\% \\ \hline \multirow{4}{*}{[PERSON] et al. [63]} & AlexNet & 2 classes & Kaggle & 35,000 & Sensitivity & 95\% * \\  & GoogleNet & - & Messider & 1200 & Specificity & 96\% * \\  & VGG16 & - & - & - & - & - \\ \hline \multirow{4}{*}{[PERSON] et al. [63]} & AlexNet & \multirow{4}{*}{5 classes} & \multirow{4}{*}{ Kaggle} & \multirow{4}{*}{35,126} & \multirow{4}{*}{AUC} & 93.42\% \\  & VGG-S & & & & **97.86\%** \\  & VGG16 & & & & 96.16\% \\  & VGG19 & & & & 96.84\% \\  & GoogLeNet & & & & 92.72\% \\  & ResNet & & & & 93.65\% \\ \hline \multirow{2}{*}{[PERSON] et al. [64]} & DenseNet & 5 classes & Private & 20,000 & Error rate & 17.48\% * \\ \hline \multirow{2}{*}{[PERSON] et al. [65]} & InceptionV3 & 2 classes & Kaggle & 2500 & Accuracy & 90.9\% \\  & InceptionV3 & 5 classes & Kaggle & 2500 & Loss & 3.94\% \\ \hline \multirow{2}{*}{[PERSON] et al. [67]} & InceptionV3 & 2 classes & Kaggle & 7023 & Kappa & 0.64 \\  & InceptionV3 & 5 classes & Kaggle & 28,104 & Accuracy & 80\% \\ \hline \multirow{2}{*}{[PERSON] et al. [68]} & ResNet & \multirow{2}{*}{4 classes} & \multirow{2}{*}{Private} & \multirow{2}{*}{13,767} & Kappa & 0.829 \\  & DenseNet & & & & AUC & 95.1\% \\ \hline \multirow{2}{*}{[PERSON] et al. [69]} & VGG16 & \multirow{2}{*}{3 classes} & \multirow{2}{*}{Private} & \multirow{2}{*}{148,266} & Sensitivity & 98.18\% * \\  & ResNet & & & & Specificity & 98.9\% * \\ \hline \multirow{4}{*}{[PERSON] et al. [70]} & InceptionV3 & \multirow{4}{*}{4 classes} & \multirow{4}{*}{Private} & \multirow{4}{*}{4476} & \multirow{4}{*}{Accuracy} & **88.72\%** \\  & InceptionV3 & & & & & 88.35\% \\ \cline{1-1}  & ResNet181 & & & & 87.61\% \\ \cline{1-1}  & VGG19 & & & & 87.26\% \\ \hline \hline \end{tabular}  
* The results from [PERSON] et al. [55] are the results of fine-tuning the entire networks by using the Messidor dataset. The results from [PERSON] et al. [68] are the results of the grading model. The results shown from [PERSON] et al. [62] are the results of GoogLeNet architecture for the two-class Kaggle dataset. The results from [PERSON] et al. [69] are the results of the vision-threatening DR. The results shown from [PERSON] et al. [64] are the results of using transfer learning with 24 kernels. The VGG19 results shown from [PERSON] et al. [58] are the VGG19 with transfer learning and RF as a classifier.  
\end{table}
Table 3: _Cont._