# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 4 Results
### State-of-the-Art Comparisons  
Few research attempts used deep learning methodologies for identifying HR among retinal pictures. In this research project, two deep learning models such as Triwijoyo-2017 [7] and Pradipto-2017 [33] were selected to compare to the results of our developed system. Table 7 shows how the proposed CAD system compares to other deep learning approaches (Triwijoyo-2017 [7] and Pradipto-2017 [33]). The Triwijoyo-2017 system [7] was trained using (32 \(\times\) 32) scaled gray-level batches translated out of a big fundus set of 9500 retinal pictures, 5000 of which were non-HR and 4500 of which had HR signs. The CNN produced either HR or non-HR decisions. We implemented a similar model to that in [7]. Table 7 presents the results of several DL approaches on this 3580-image collection. The performance of the Triwijoyo-CNN-2017 and our systems were quantitatively compared with regards to of SE, SP, ACC, and AUC indicators. Considering 3580 retinal pictures, the Triwijoyo-2017 system achieved 78.5%, 81.5%, 80%, and 0.84 for those metrics, respectively. The CNN architecture proposed in the Triwijoyo-CNN-2017 system fails to exert operative and broad features that can be used to distinguish between HR and non-HR. Consequently, in comparison, the developed CAD-HR system produced better results, with of 94%, 96%, 95%, and 0.96 for SE, SP, ACC, and AUC, respectively. The authors of this research project cited the identification accuracy of 98.6% in Tiwijoyo-2017 [7]. But we notice that they used a very restricted set of input fundi for training, as they only contain 40 retina photos, 20 of which are normal and 20 of which are HR which lead to the high level of precision. Accordingly, our CAD-HR system was tested and trained on a huge dataset. Therefore, we attained a classification accuracy of 95%. The CNN-RBM model was only used as a classifier in the Pradipto-2017 [33] system. For subdivision and excavation of features, the system engages image processing algorithms. The classification stage input was a feature vector consisting of A/V Ratio and OD, more willingly than the retinal images. We used the same processes and tested them on our \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **1 SE** & **2 SP** & **3 ACC** & **4 AUC** \\ \hline Triwijoyo-2017 [7] & 78.5\% & 81.5\% & 80\% & 0.84 \\ Pradipto-2017 [33] & 81.5\% & 82.5\% & 84\% & 0.86 \\
**Proposed CAD-HR model** & **94\%** & **96\%** & **95\%** & **0.96** \\ \hline \hline \end{tabular}  
* SE: Sensitivity, 2 SP: Specificity, 3 ACC: Accuracy, and 4 AUC: Area under the receiver operating curve  
\end{table} 
Table 7: Performance of the developed CAD-HR system compared to other DL systems on 9500 samples. Figure 13: (**a**): A visual representation of accuracy and loss on training and validation on Imam-HR and (**b**) Confusion matrix of implemented CAD-HR on Imam-HR. dataset of 3580 photos to compare with Pradipto 2017 [33]. Table 7 shows that our system significantly outperformed the Pradipto-2017 model too. In this paper, architecture composed of one DL method called _DSC_ with LSVM was constructed by means of a _DSC_-LSVM technique. Therefore, we were able to achieve greater accuracy against recent HR detection approaches. One more stage was integrated to the architecture in the improved _DSC_ model, three residual connections, which contained localized and specialized features, respectively. In comparison to features recovered by the Triwijoyo-CNN-2017 [7] and Pradipto-2017 [33] models, these _DSC_ with three residual attributes were more generic. We updated residual connections with skip paths to resolve the issue of extracting HR-related grazes using a fresh training technique rather than employing pre-train architectures to create features. Moreover, we have compared it with three other transfer learning-based (TL) CNN architectures, such as VGG-16, VGG-19, ResNet-50, Inception-v3 and AlexNet. To assess the performance, the preprocessing and data augmentation techniques were first applied to these TL architectures.