# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
## 2 Related Studies
### Handcrafted Studies  
According to a survey of the literature, detecting hypertensive retinopathy (HR) from fundus images should be done using a segmentation-based methodology [12]. The scientists employed a machine-learning classification technique to identify HR from retinography photos in their experiments after first detecting distinct HR-related characteristics. In [13; 14; 15; 16; 17; 18; 19], the artery to vein diameter ratio (A/VR), the optic disc (OD) location, mean fractal dimension (mean-D), papilledema indicators, and index of tortuosity are hand-crafted features. Several approaches used these features to measure the irregularities in the retina. Aimed at subdivision and neat tasks, the Gabor 2D or Cake-Wavelet were utilized, together with the Canny edge detector. To evaluate the performance of such systems, the DRIVE, AVR-DB, IOSTAR, STARE, DR/HAGIS, INSPIR/AVR and VICAVR datasets were employed. In [19], they used a supervised classifier to perform initial segmentation and refinement processes to find hemorrhagic lesions. The authors in [20] used a new technique for detecting HR-related eye illness. Cotton wool spots (CWS) were discovered and are among the more significant medical indications for determining HR illness. In the study, the authors enhanced candidate regions and then binarized the image with the adaptive threshold approach. However, in paper [21], the authors achieved 82.2% of SE and 82.4% of PPV on a local dataset. They used a neural network (NN) and wavelet transform (WT) methods to detect all mentioned retinal diseases. Classification accuracies of 70% for HR were achieved. The AVR technique was used on a few color retinography pictures from ETDRS and DCCT datasets in the systems described in [22; 23; 24; 25]. The A/V ratio was computed by segmenting the OD area and then classifying vessels as veins and arteries using the following operation gradients, morphological edge-detection, and Gabor/wavelet. An alternative technique is segmenting the OD area and classifying vessels as veins and arteries using the morphological edge-detection operation. In [26], a system with a GUI was created to perform half-automated retinal vascular recognition and assess the performance of HR illness identification. This GUI tool was also utilized for assessing the accuracy of determining the diameter of vessels anywhere of interest (ROI) to help people with HR understand their vascular risk. This tool was evaluated with the aid of the STARE and DRIVE datasets, and it performed well in classifying tasks. A clustering-based method to detect arteries and veins was used in combination with the A/V ratio in [27]. Alternatively, in [28], the moments and gray-level characteristics were retrieved as features to recognize fundus areas that fit to vessels. Differentiation between veins and arteries was achieved using color data as well as variation in intensity. On 101 images from the VICAVR dataset, various phases of HR-related eye illness were categorized using vessel width estimation. Two kinds of transforms were used, Hough as well as Radon, to segment retinal vessels, before estimation of the vessel diameter and tortuosity-index, and finally computation of the A/V ratio to detect HR from input retinography pictures [29]. Furthermore, in [30], an independent component analysis (ICA) was performed to a wavelet sub-band for identifying retina. In the study, the authors include different lesions such as optic disc, blood vessels, hemorrhages, macula, exudates and drusen. This approach was put to the test on 50 images. To categorize the different retinal blood vessel types, the authors in [31] used a several layered neural network that was supplied with invariant moments indicators. In [32], the researchers describe a nine-step automated system for extracting the different HR-related lesions. The 0.84 of AUC was attained on a collection of 74, while a 90% sensitivity and 67% specificity were achieved.