# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 1 Introduction  
The high blood sugar levels in diabetes can damage the small blood vessels in the retina, and when this is combined with the high blood pressure in hypertension, it can further damage the blood vessels, leading to bleeding and fluid leakage in the retina. This can cause vision problems, such as blurred vision, dark spots or floaters, and even vision loss if left untreated. Management of diabetic hypertensive retinopathy involves controlling both diabetes and hypertension through lifestyle changes and medication. Regular eye exams with an eye doctor (ophthalmologist) are also important to monitor any changes inthe retina and to detect any issues early on. Treatment may include laser therapy, injections or surgery, depending on the severity of the condition. It is important for individuals with diabetes and hypertension to work closely with their healthcare team to manage their conditions and reduce the risk of complications, including diabetic hypertensive retinopathy [1]. Computer aided diagnosis (CAD) has evolved as a major research problem for medical imaging and diagnosis of radiology. The field of medical imaging uses image information to evaluate and analyze abnormalities at an early stage. In CAD systems, low-contrast medical images are being improved with diverse imaging techniques that are designed to interpret prominent portions of the image and are relevant for the diagnosis of diseases [2]. CAD systems are computer systems designed to detect or diagnose diseases by providing clinical experts with a second opinion. It improves overall performance for the detection of diseases and helps doctors detect various medical abnormalities at an early stage. CAD systems are specifically intended to improve disease diagnostic accuracy and reduce computation complexity in interpreting image information. The same role of clinics and computer-aided algorithms is considered, and better diagnostic performance is achieved. CAD systems are also useful in fundus image analysis as, by regular screening and prompt treatment, the likelihood of severe eye complications is reduced. This research aims to provide a CAD scheme to help ophthalmologists distinguish various grades of DR and HR with a second opinion [3]. Automatic extraction and convolutional neural networks (CNNs) are two concepts in the field of artificial intelligence and machine learning that are often used together in image and video processing tasks. Automatic extraction, also known as feature extraction, refers to the process of identifying and selecting relevant features from raw data. In image and video processing tasks, these features could include edges, textures, shapes, or other patterns that can help distinguish one object from another. Convolutional neural networks, on the other hand, are a type of deep learning algorithm that are commonly used in image recognition tasks. They are designed to automatically learn and extract features from images by passing them through a series of convolutional layers. These layers consist of a set of filters or kernels that convolve with the input image, detecting features at different scales and orientations [4]. The output of each convolutional layer is then passed through a pooling layer, which downsamples the feature maps to reduce the number of parameters and prevent overfitting. The final output is a set of high-level features that can be used to classify or label the input image. Since the pre-trained network consists of various filters, each layer consists of diverse features. A large classifier is necessary to handle these features. A deep network classification is usually made up of two hidden layers with 4096 neurons. In this manner, the weight parameter of the classifier is millions. It is a challenging task to train this independent classifier. A network that could extract an optimum number of features is therefore required. In addition, an adaptive classifier structure to deal with these extracted characteristics is also necessary.