# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 4 Results
Sensitivity, specificity, accuracy, and AUC metrics were utilized to compare the recital of traditional CNN, DRL, trained-CNN and trained-DRL models to the developed CAD system, as shown in Table 6. \begin{table}
\begin{tabular}{c c} \hline \hline
**Convolutional Layers** & **Parameters** \\ \hline Conv-32 & \((3\times 3\times 3+1)\times 32\) \\ Conv-32 & \((3\times 3\times 32+1)\times 64\) \\ Conv-64 & \((3\times 3\times 64)+(1\times 1\times 64+1)\times 128\) \\ Conv-64 & \((3\times 3\times 128)+(1\times 1\times 128+1)\times 128\) \\ Conv-128 & \((3\times 3\times 128)+(1\times 1\times 128+1)\times 256\) \\ Conv-128 & \((3\times 3\times 256)+(1\times 1\times 256+1)\times 256\) \\ Conv-256 & \((3\times 3\times 256)+(1\times 1\times 256+1)\times 728\) \\ Conv-256 & \((3\times 3\times 728)+(1\times 1\times 728+1)\times 728\) \\
**Total** & **87,488** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parametric configuration of the convolutional layer of proposed CAD-HR model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hypertenseive Type** & **1 SE** & **2 SP** & **3 ACC** & **5 AUC** & **4 E** \\ \hline Diabetic Hypertension (HR) & 93\% & 96\% & 94\% & 0.95 & 0.76 \\ Non-hypertension (non-HR) & 95\% & 96\% & 95\% & 0.96 & 0.67 \\
**Average Result** & **94\%** & **96\%** & **95\%** & **0.96** & **0.60** \\ \hline \hline \end{tabular} \({}^{1}\) SE: Sensitivity, \({}^{2}\) SP: Specificity, \({}^{3}\) ACC: Accuracy, \({}^{4}\) E: Training errors and \({}^{5}\) AUC: Area under the receiver operating curve.  
\end{table} 
Table 5: Performance evaluation metrics of the implemented CAD-HR system on 9500 retinal samples. On this dataset, the trained-CNN model performed well, with the following values: 81.5%, 83.2%, 81.5%, and 0.85 for SE, SP, ACC and AUC, respectively. Metrics' results of 82.5%, 84%, 83.5%, and 0.86 for SE, SP, ACC and AUC, respectively, were attained with the DRL model. The developed CAD-HR system obtained higher results than deep-learning models by merging the capabilities of \(DSC\) on four annotated fundus sets with residual connections that are not disposed to overfitting difficulties. **Experiment 4:** We begin to test our proposed \(DSC\)-CNN model on the DRIVE and DiaRetDB0 datasets using LSVM training accuracy and validation accuracy, coupled with training loss and validation loss function. As can be seen in Figure 11a,b our proposed model exhibits great performance and obtained a training accuracy and validation accuracy of over 100% while employing a total of only ten iterations of the training and validation processes. Additionally, we were able to acquire a very low loss function for both the training data and the validation data that was below 0.1, which is evidence that our proposed strategy is effective. To adequately evaluate classification performance, we must first collect the confusion matrix. The discordance between the predicted and real labels was revealed by the confusion matrices. The column labels showed the anticipated label for each column, while the row labels showed the genuine label for each row. Consequently, based on the test set identified by our model, we were able to acquire the results of two categories, namely, HR and non-HR samples. Using 110 samples from the DRIVE and DiaRetDB0 datasets, we evaluated the performance of our model. The confusion matrix in Figure 11b makes it abundantly evident that our suggested model successfully identified every HR image utilized in the DRIVE and DiaRetDB0 datasets. Even with the limited dataset training sample for the model, it might be discovered that the p label for each category does not become