## 3 (DRSM-CNNFIS) Framework
### Dataset Description  
Different retinal image types and qualities are available for developing and testing digital screening for diabetic retinopathy. The public dataset used in this work was Kaggle Diabetic Retinopathy Detection [10], which is sponsored by the California Healthcare Foundation.  
The Kaggle DR Dataset has 35,126 fundus images for training. Different devices collected the images. Kaggle DR is one of the largest publicly available DR classification datasets; most of the labelling was performed manually and the quality of the images is not homogenous.  
These images were labeled using a scale of 0 to 4 based on the severity of diabetic retinopathy (DR). Table 2 shows the five classes of DR as well as their respective percentage from the total data. According to the international clinical diabetic retinopathy scale [35], binary classification, images with labels of 0 and 1 were classified as \"No PDR\", and relabeled with 0, and images with labels of 2, 3, and 4 were classified as \"RDR\" and relabeled with 1, as shown in Table 3. The distribution of labels was: [0:25,810, 1:2443, 2:5292, 4:708, 3:873]. Total images: 35,126.  
Table 2 provides the five grades of the dataset with their percentage, and Table 3 provides the distribution of the binary classification in the training set of the Kaggle dataset. Figure 1 shows sample gradable images from Kaggle DR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **References** & **Data Used** & **Performance (Accuracy)** & **Limitation** \\ \hline Ensemble & [36] & (APTOS 2019 BD) dataset. & Accuracy of 94.20\%. & Noisy images, duplicate images with improper labelling, uneven image resolution, and varying class sample sizes. \\ \hline AlexNet, VggNet, GoogleNet, ResNet, & [28] & Kaggle fundus images data were increased to 20 times the original. & The best classification accuracy is 95.68\%. & Huge training dataset. \\ \hline Resnet50, Inceptionv3, & & \\ Xception, Dense121, & [29] & Same Kaggle dataset. & The best classification accuracy is 80.8\%. & Very long training time. \\ Dense169 & Ensemble & & \\ \hline DRSM-CNNFIS & — & Kaggle dataset increased to 3.6 times the original. & The best classification accuracy is 92.8\%. & Need for a human domain expert for rule and framework fine-tuning. \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Class** & **Number of Samples** & **Percentage** \\ \hline
0 & Normal & 25,810 & 73.84\% \\
1 & Mild NPDR & 2443 & 6.96\% \\
2 & Moderate NPDR & 5292 & 15.07\% \\
3 & Sever NPDR & 873 & 2.43\% \\
4 & Proliferative DR & 708 & 2.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of multiclass classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Class** & **Number of Samples** & **Percentage** \\ \hline
0 & Normal & 25,810 & 73.84\% \\
1 & Mild NPDR & 2443 & 6.96\% \\
2 & Moderate NPDR & 5292 & 15.07\% \\
3 & Sever NPDR & 873 & 2.43\% \\
4 & Proliferative DR & 708 & 2.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of binary classification.  
In this research, the class imbalance issue was addressed using two methods--first, data augmentation, as explained in Section 3.2, and human experts' rules for the results, as explained in Section 3.3.