# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 3 Materials and Methods
### Proposed DL Architecture  
A novel classification approach named _DSC_-LSVM is presented to differentiate HR-related classes, as depicted in Figure 7. To generate the feature map, this architecture uses two convolutional layers of kernel size three, three Max Pooling layers, eight Bach Normalization (BN) layers, three residual blocks with two separable convolutional layers, and one convolutional layer of kernel size one. Following that, one flattened layer, one dense layer, and an SVM classifier with a linear activation function were used for classification. Figure 6: An example of the difference between the traditional ML approach and the TL method [66], where Figure (**a**) shows the task and (**b**) represents the target related knowledge. For separable convolutional layers, the kernel size was set to three, with border padding of two and stride of two. In the pooling layers, a kernel size of three and a stride of two were also employed. Using Batch Normalization after each separable convolution layer before the activation function could improve the efficiency and stability of deep neural network training [75]. Additionally, the rectifier linear unit exponential linear units (RELU) function is used to define the output value of the kernel weights and convolutional layer for each separable convolutional layer. It offers a negligible value that enhances the performance of categorization. Finally, a linear SVM (LSVM) classifier was used to categorize the fundus  
Figure 7: A visual representation of the proposed CAD-HR-diagnosis system utilizes a DSCNN, residual connections and LSVM to extract features and classify them for HR stage classification. sample into true and false images after the feature had been flattened, densely packed, and reduced in size. Algorithm 1 provides a description of the mechanism that underpins the suggested network model for the feature extractor. The proposed work uses the _DSC_ instead of the regular convolution structure to reduce the number of calculations while keeping the CNN network's generalization. This is shown in Figure 8. The _DSC_ model divides the calculation of the convolution into two stages: (1) depth-wise convolution was used to filter each channel, and (2) point-wise convolution was used as the kernel for combining. The outputs of the depth-wise convolution were then combined using a point-wise convolution. A criterion convolution generates a new output set by simultaneously filtering and merging the input. This new art reduced the computation time and parameter size. Equation (6) shows how to compute the total size of a single convolutional layer of _weights_ (in bytes) [76]. \[weights=\text{C}\;\times\;\text{Nf}\;\times\;\text{Lf}\;\times\;\text{Wf} \tag{6}\]  
where C represents the total number of channels, Nf indicates the total number of filters, Lf indicates the length of the filter, and Wf indicates the width of the filter. The computation of the parameters in the separable convolution layer is shown in Equation (7). By utilizing the separable convolutional layer, the number of parameters was reduced compared to the original CNN. \[weightDSC=\text{Lf}\;\times\;\text{Wf}\;\times\;\text{CL}-1+\;\text{CL}\times 1 \times 1\times\text{Nf} \tag{7}\]  
where L indicates the convolution layer, C shows the total number of channels, Nf indicates the total number of filters, Lf represents the length of the filter, and Wf indicates the width of the filter. The _DSC_ offers two advantages for constructing a deep learning model: (1) it can help minimize parameters, and (2) it can be used to improve model generalization. Thus, it was reasonable to assume that _DSC_ improved detection accuracy and training effectiveness. The residual connection, which can also be referred to as a skipped connection, allows a network to avoid using either two or three of its levels. Figure 9 depicts the residual  
Figure 8: A visual example of Depth-wise separable CNN layer structure. single link block in the deep learning network.