# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 4 Results  
To assess the effectiveness of the proposed deep learning-based CAD-HR approach, in all experimental trials, datasets were split into a training set and testing set with a 3:1 split, meaning that three-quarters of the data is used for training and the remaining one-quarter is used for testing. Three separate internet and one private source provided these retina images. To execute feature extraction and classification tasks, all 3580 photos were scaled to (700 \(\times\) 600) pixels. Our CAD system is created by combining the two deep learning techniques named DSCNN with residual connection, and linear SVM. A computer with a core i7 processor, 16 GB RAM, and a 4 GB Gigabyte NIVIDA GPU was utilized to implement and program our CAD-HR system. Deep learning libraries named TensorFlow (version 2.7) and Keras with windows 10 professional 64-bit edition are installed on this PC. Various kernel dimensions are used in order to produce feature maps from the preceding stage to create/train the CNN architecture. Because the kernel dimensions (3 \(\times\) 3 or 5 \(\times\) 5) are generally used in this project, the convolutional layer's weights parameters are also changed. The convolutional layers are convoluted using varied window sizes and values acquired from each feature map's excitation objective function. As a convolutional layer, a similar process was utilized to create the pooling layer. There is only one difference: to optimize the characteristics gained from the previous layer, sliding steps of two and a window size of 2 \(\times\) 2 are used. This phase boosts the network's overall speed while lowering the convolutional weights. The output of this average pooling is fed into a LSVM that is fully connected. This FC stage is employed for distinguishing among HR and not-HR situations. Table 4 illustrates the number of parameters in the convolution layer of the proposed CAD-HR model. To assess the proposed CAD system's performance, statistical analysis was used to calculate the accuracy (ACC), specificity (SP), and sensitivity (SE) values. These metrics are utilized to evaluate the generated CAD system's performance and to compare it to previously developed systems [48]. This section provides a comprehensive discussion on the various experiments performed to assess the efficiency of the proposed CAD-HR model. These experiments are categorically demonstrated in the subsequent paragraphs. **Experiment 1:** A cross validation testing scheme of 10 fold is utilized in the experiment 1 for comparing the resulted AUC metric to other DL methods. Overall, the AUC metric was primarily utilized to judge the categorization accuracy. The developed CAD system's performance has been quantitatively measured as shown in Table 5. The developed system achieves very good outcomes with regards to SE (94%) and SP (96%), as well as a lower training error (0.76) in identification of HR eye illness. **Experiment 2:** Several studies were carried out in experiment 2 to illustrate the efficacy of the proposed CAD system in detecting HR from input retina images. The optimum network design for retinal images HR categorization tasks was determined through experimentation. The developed CAD system shows the efficacy of the feature learned based on the optimum network design. Figure 10 shows a visual example of classification results for various input fundi. Figure 10a shows HR-classified retinography samples with various signs such as cotton wool patches and hemorrhages. A non-HR fundus picture is also shown in Figure 10b. **Experiment 3:** On the retinal fundus datasets with pre-processing, the training phase is accomplished in various CNN and DRL architectures with eight to sixteen stages to perform comparisons. Table 5 summarizes the findings. It is worth noting that all the CNN and RNN deep-learning models used the same number of epochs in their training. With a validation accuracy of 59%, the highest performing network is chosen, and identical traditional Convolutional Networks were trained.