# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 3 Materials and Methods
### CNN and Transfer Learning  
Recently, DL frameworks have surpassed conventional methods in various computer vision problems, such as iris recognition [66], face detection [67], classification [68], and many more. According to the findings of these studies, the CNN performs better than traditional methods. Figure 5 shows the fundamental notion of CNN. To categorize the input image into predetermined classes, the CNN uses a neural network (shown in Figure 5 as fully connected layers) that uses feature vectors to extract image features. Even though CNN has obtained remarkable results for many image-based systems, it has several problems. The two key issues associated with the CNN model are the expense in performing computations and overfitting. As CNN requires a long time to process, it is challenging to implement CNN models on a single general-purpose machine comprising fewer central processing units (CPU). Fortunately, the introduction of graphical processing units (GPU) [69] has solved this problem. By employing numerous CPUs running in parallel and GPUs, CNN can be used in real-time systems. Another issue associated with CNNs is over-fitting. The CNN, as previously discussed, is developed by learning millions of trainable parameters. Consequently, CNN-based systems often require an enormous amount of training data. Although this problem has been addressed in several techniques, including data augmentation and dropout, a massive amount of data is still used in such CNN systems for training. This problem was recently solved using transfer learning (TF) art [70]. With the TF method, we can use a CNN, trained with sufficient data for one problem to solve a different problem. This strategy has been proven useful for many problems, particularly when significant training data, such as medical images [71], are limited. Figure 4: A visual example of the proposed data augmentation. Figure 5: A visual example of the typical structure of CNN. Since 1995, research that is based on transfer learning has seen an increase in popularity. This type of research is also well-known by the name of multitask learning [72]. Multitask learning is a method of learning numerous tasks simultaneously that is relatively equivalent to other learning schemes for the objective of knowledge transfer [73]. Figure 5 shows how a standard ML technique for various tasks differs from a transfer learning method for the same tasks. The first task in transfer learning is trained on large benchmarks, and then the second task takes advantage of the knowledge gained from the first task to learn more quickly and improve its accuracy. Based on the availability of annotated data, TF tasks are classified into mainly inductive, transductive, and unsupervised transfer learning. Usually, we employed a pre-trained deep learning model, one of the most common types of inductive TL strategy that employs source domain and job, to fine-tune different layers for the target goal. This allowed automated algorithms to get the results more precisely. There are two ways that the target predictive function FT(.) in the target domain can be improved: first, it can be improved by combining the knowledge from the source domain DS and a task learning task LTS, and second, it can be improved by utilizing the knowledge from the target domain DT and a task learning task LTT, where DS \(\
eq\) DT \(\
eq\), or LTS \(\
eq\) LTT. A comparison of the classic ML method and the TL methodology is shown in Figure 5(a,b). The target task (\"Target Task\") in Figure 5(b) and knowledge (model) generated from another ML problem are the two sources from which the TL technique learns system information. In a traditional ML system, the system model is only learned for a particular task utilizing input from a single source (as shown in Figure 6). A CNN can be reused and applied to a new task using the transfer learning method. To establish the proposed CNN architecture of our work, we modified the architecture and employed the idea of the Xception model [74] for our experimental work.