# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 2 Related Studies
### Methodological Overview  
Feature extraction and recognition tasks based on deep learning models (DLMs) have been widely used in previous systems to recognize objects [40; 41; 42; 43; 44]. A novel active deep learning-based convolutional neural network (ADL-CNN) was implemented to overcome multilayered network architecture training challenges [45]. In practice, the ADL-CNN architecture is simple to train [46; 47; 48; 49]. In addition, the CNN model has been applied in a great amount of research for the purpose of picture recognition on a broad scale [50]. The ADL-CNN model is unsurpassed in performance when compared to other models for use with minimal training datasets [51; 52]. In previous studies, the researchers reported that the CNN model automatically learns distinguishing features from the raw samples [53]. In the past, the most common methods for identifying HR-related lesions within retinal images were conventional image processing techniques as well as machine learning algorithms. However, those traditional arts are slow and difficult to apply in real-time environments [54]. With the development of hierarchical deep learning methods, it is now possible to tackle all of the previously described issues. There are a wide variety of \begin{table}
\begin{tabular}{c c c} \hline \hline
**DL Studies** & **Datasets** & **Limitations** \\ \hline HR Identification directly from fundus using & DRIVE & Very limited dataset \\ HN Identification using traditional feature & DRIVE & No statistical results were shown \\ vector and CNN-RBM classifier [33] & STARE & \\ Four pretrained CNN to extract attributes & DRIVE, DR-HAGIS and Private dataset & Not classifying HR into its four \\ from 4 diverse HR lesions then feed to a & of 3170 fundus images & grades just HR or normal \\ classify fundus into HR or normal [35] & DRIVE, EPIC (100 fundus), UK Biobank & Datasets used are very limited \\  & (100 fundus) & Datasets used are very limited \\ Detect exudates in microscopic retinographs. & DRiDB & Datasets used are very limited \\  & & \\   using CNN [40] & & \\  & & \\   from 4 diverse HR lesions then feed to a & DRIVE, DR-HAGIS and Private dataset & Not classifying HR into its four \\ DRN to generalize those features and then & of 3170 fundus images & grades just HR or normal \\ classify fundus into HR or normal [42] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deep learning (DL)-based techniques for retinography photos’ processing and classification. deep learning approaches, including convolutional networks, deep belief networks (DBN), reinforcement learning machines (RBM), and deep reinforcement learning network (DRN) models [55, 56]. The pre-training procedure is applied in a wide variety of computer vision applications to acquire characteristics for the purpose of resolving a variety of issues [57]. The transfer learning technique is an example of the pre-trained networks used in numerous computer vision systems. There are several pre-trained models that have been developed, including VGG [48], InceptionV3 [56], and ResNet50 [43]. The ImageNet dataset was utilized in the performance evaluation process of these pre-trained networks [58, 59]. A substantial number of pre-trained models utilized in transfer learning are based on large CNNs. The high performance and easy training of CNN have raised CNN's popularity level in recent years [53, 60]. A basic CNN model's architecture consists of two portions, including a convolutional base composed of a features map and pooling layers. Convolutional filters of various sizes were used to create the features map layer. In addition, the SoftMax classifier, which recognizes classes, is a second component. It is common for the classification layer to be fully interconnected in practice. The features extracted during the first layer of CNN's deep learning architecture are more generic, whereas the features defined during the last layer are the most specialized [61].