# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 5 Discussion  
CAD-HR was implemented by utilizing a trained CNN model as an input to \(D\)SC architectures to classify HR using three consecutive residual blocks and a linear SVM. The learning process of specialized features was completed by introducing a multilayered hierarchical framework without utilizing complex image processing and feature selection methods. This multi-layer architecture learnt features directly from the input image with the learning methods, eliminating the need for human intervention. The Xception model is modified to include \(DSC\) and residual blocks to create more generalizable features to develop CAD-HR architecture. Based on a scratch-based training technique, the depth-wise convolutional layer obtains localized and trained features from four HR-related lesions. The CNN model for learning deep features is made up mostly of convolutional, pooling, and fully connected layers. To construct this model, those layers must be trained and found to be effective in extracting useful information. These features are not optimal for recognizing HR in retinography pictures. As a result, deep residual connections were integrated, which provided highly specialized features rather than feature-based categorization methods that required human intervention. The success in detecting HR was made possible by an autonomous features learning method. For the diagnosis of HR disease, however, the hand-crafted based classification techniques rely on the pre-processing, segmentation, and localization of HR-related data, which are computationally expensive algorithms. Compared to other crucial symptoms like cotton wool spots or hemorrhage recognition, researchers only exerted considerable attention to extract the necessary elements. In the past, a few classification systems for HR-related and non-HR-related eye diseases were developed, as described in Section 2. These systems use deep-learning methods instead of traditional machine learning arts. No datasets with clinical expert annotations defining these HR-related lesion patterns are available for training and evaluating the network. Therefore, it is challenging for computerized systems to identify these illness traits. According to the available literature, the authors used manually constructed features to train the network and assess the performance of standard and cutting-edge deep learning models. Consequently, an automatic technique is necessary to identify optimal features. Compared to the conventional approach, deep-learning models produce superior outcomes. However,  
Figure 14: Comparison with other transfer learning-based (TL) CNN architectures for classification of HR based on data augmentation and preprocessing steps. other models utilized trained models built from scratch to automatically learn features, but they all shared the same weighting technique at each level. Subsequently, it may be difficult for layers to transfer accurate decision-making weights to deeper network levels. The CAD-HR system is created in this study to overcome the problems by classifying images into HR and non-HR using two multi-layer deep learning techniques rather than concentrating on image processing algorithms. The CAD-HR system's significant contributions are listed here. This research developed two new deep learning arts using a convolutional neural network (CNN) and residual blocks. Four distinct HR lesions were used to train the first CNN model, which was then used to determine the hierarchy of features. To enhance the efficiency of the learning procedure, the second residual blocks were used to identify the feature maps with the most useful information. It is the first system built in this study for the purpose of classifying HR, and it is based on perceptually oriented color space. The deep features are classified utilizing the _DSC_ model and LSVM classifier. According to our knowledge, this is the first attempt to identify HR illness automatically.