# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 1 Introduction
Diverse feature extraction procedures were applied for image classification. A feature is known as the object's property that is defined by one or more functions. It can also help to reduce the amount of redundant data in retinal images. Texture features are useful in image analysis for accurate classification. It provides more information about the spatial arrangement of the pixels in terms of color and intensity. Furthermore, vascular abnormalities are the most common signs of DR. Since the texture properties of fundus images are retrieved for MA detection, the automated algorithm's accuracy is improved [5]. Diabetic retinopathy occurs in patients with a history of diabetes for around 5 years or so. Diabetic retinopathy (DR) is a major cause of blindness found in diabetic individuals. DR can be caused by diabetes mellitus that affects the retinal microvasculature. Therefore, there is a need to detect DR in the early stages to reduce the risk of blindness in working-age groups [6]. DR can be detected by taking the fundus images of the retina. Detection of morphological changes that include microaneurysms, hard exudates, soft exudates (cotton wool spot), hemorrhages, macula, optic disk, optic nerve head and an increase in the blood vessels in fundus images are still a tedious task. These morphological changes can be detected either by time-consuming manual inspection or by computer-aided diagnosis (CAD) that can help the ophthalmologist to identify the problem. Hypertensive retinopathy (HR) occurs due to hypertension (high blood pressure) which affects the blood vessels. The symptoms of HR initiate with AVR nicking, tortuosity and bifurcation in blood vessels in the early detection of disease [6]. This paper focuses on the development of a computationally capable system for grading the severity of DR and HR retinal anomalies. The developed methodologies and models can correctly discern retinal physiological landmarks, diagnose pathological symptoms and effectively characterize DR and HR severity classes. The retinal image classification is performed using three phases that include preprocessing, segmentation and feature extraction techniques. The pre-processing methodology is proposed for reducing the noise in retinal images. A-CLAHE, DNCNN and Wiener filter techniques are applied for the enhancement of images. After pre-processing, blood vessel segmentation in retinal images is performed utilizing OTSU thresholding and mathematical morphology. Feature extraction and classification are performed using transfer learning models. The segmented images are then classified using Modified ResNet 101 architecture. The potential work of numerous authors on diabetic retinopathy detection, a hypertensive retinopathy detection based on transfer learning, is described in Section 2. Section 3 discusses the methodology of three phases, namely pre-processing, segmentation and feature extraction. In the pre-processing phase, a novel hybrid technique is implemented for denoising the images. An efficient method for the segmentation of blood vessels is discussed after enhancing the images. The ResNet-101 model, which is a pre-trained network architecture, is used to classify the segmented images. Section 4 describes the results and discussion of the proposed techniques followed by conclusion and a future scope in Section 5.