# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
###### Abstract  
Hypertensive retinopathy (HR) is a retinal disorder, linked to high blood pressure. The incidence of HR-eye illness is directly related to the severity and duration of hypertension. It is critical to identify and analyze HR at an early stage to avoid blindness. There are presently only a few computer-aided systems (CADx) designed to recognize HR. Instead, those systems concentrated on collecting features from many retinopathy-related HR lesions and then classifying them using traditional machine learning algorithms. Consequently, those CADx systems required complicated image processing methods and domain-expert knowledge. To address these issues, a new CAD-HR system is proposed to advance depth-wise separable CNN (_DSC_) with residual connection and a linear support vector machine (LSVM). Initially, the data augmentation approach is used on retina graphics to enhance the size of the datasets. Afterward, this _DSC_ approach is applied to retinal images to extract robust features. The retinal samples are then classified as either HR or non-HR using an LSVM classifier as the final step. The statistical investigation of 9500 retinograph images from two publicly available and one private source is undertaken to assess the accuracy. Several experimental results demonstrate that the CAD-HR model requires less computational time and fewer parameters to categorize HR. On average, the CAD-HR achieved a sensitivity (SE) of 94%, specificity (SP) of 96%, accuracy (ACC) of 95% and area under the receiver operating curve (AUC) of 0.96. This confirms that the CAD-HR system can be used to correctly diagnose HR. retinography images; hypertensive-retinopathy; deep-neural network; transfer learning; color space transformation; residual neural network; convolutional neural network +
Footnote †: journal: _Sciences_  
of HR. HR is notoriously challenging to detect in its earliest stages because there is a dearth of modern imaging equipment and trained ophthalmologists. Generally, HR is an abnormality in the retina triggered by an excessive rise in blood pressure. The existence of signs such as hemorrhage (HE) spots in the retina, cotton wool patches, and micro-aneurysms are potential signs of HR-related eye illness. Early detection and proper treatment of HR-related eye illness [7] is critical to saving human life. Many researchers have recently demonstrated that retinal experts use of a digital fundus camera to acquire microscopic retinal samples to assess the presence of micro-vascular alterations caused by HR is a cost-effective and non-invasive approach. Many HR patients are non-invasively screened using this fundus camera since it is inexpensive, simple to use, and most anatomical features of lesions are visible using this type of imaging. The major goal of utilizing automated systems [8] is to determine the existence of HR while relieving ophthalmologists of the burden of vast image assessment quickly and at an early stage. Many previous research efforts have established procedures for retinal image analysis, including image enhancement, segmentation of HR lesions and retinal vessels, extracting features, and supervised machine learning classifiers for HR illness [9]. Oddly, a significant HR indication is a wideness in retinal veins, which reduces the A/V ratio (the mean artery to vein diameter). Detecting retinal vessel diameter [10] and other features such as AVR is difficult when using an image analysis system to diagnose HR-related eye disease. Furthermore, getting precise measurements of vascular diameters is quite difficult for ophthalmologists. As stated in the above paragraph, ophthalmologists use automatic analysis of digital fundus images to identify HR by observing abnormalities in retinopathy photos. These abnormalities harm HR-related lesions as well as other key eye regions. If these alterations are not noticed early enough, they can progress to HR. It was found that HR-related eye disease can be reversed [11], whereas diabetic retinopathy (DR) related eye illness cannot be reversed.