## 1 Introduction  
The human eye is an exceptional organ that provides us with the priceless benefit of vision, enabling us to connect with and experience the outside world and convey our emotions. A diabetes-related eye disease, termed diabetic retinopathy(DR), however, can impair this crucial skill. Diabetes-related high blood sugar may harm the blood vessels in the retina, impairing eyesight [1]. Currently, this is the most common cause of eyesight loss [2]. Early stages might not present any symptoms, highlighting the value of routine eye exams for early identification. Vision loss or impaired vision may present as symptoms as it advances. Diabetic retinopathy progresses in five phases. The earliest stage of no diabetic retinopathy (no DR) reveals no retinal damage. Microaneurysms occur in mild non-proliferative diabetic retinopathy (mild NPDR), causing minimal visual distortion [3]. Moderate non-proliferative diabetic retinopathy (moderate NPDR) causes vascular obstructions and decreased retinal oxygen, which can lead to the formation of new blood vessels [4]. Severe non-proliferative diabetic retinopathy (severe NPDR) is characterized by substantial oxygen deficiency and increased oxidative stress. Controlling blood sugar, hypertension, and cholesterol through dietary modifications, medication, and insulin therapy are key components of managing and preventing diabetes [5]. To monitor retinal health and enable prompt actions, routine eye exams are essential [2]. Treatments, including laser photo coagulation, intraocular injections, or vitrectomy surgery, can be necessary in extreme cases to stop future vision loss [6]. It is crucial for individuals with diabetes to take charge of managing their illness and make their sight priority number one in order to decrease the probability and effects of diabetic retinopathy [7]. The use of computer vision technologies in the detection and treatment of diabetic retinopathy offers various benefits. It facilitates early detection and increases screening accessibility [8; 9]. It makes unbiased assessments possible for individualized and affordable therapies [10]. Additionally, the data produced support research and facilitate more effective therapeutic approaches. Convolutional neural networks (CNNs), a sub-field of artificial neural networks, are widely used in computer vision tasks [11]. They are capable of learning hierarchical representations from input images, making them effective for image classification, object detection, and segmentation [12]. CNNs excel at capturing local patterns and spatial dependencies through convolutional layers, while pooling layers reduce spatial dimensions and preserve important information [13]. Despite their high performance, CNNs have limitations when dealing with small datasets. They require significant amounts of labeled data for training, which are often impractical or expensive to obtain. Additionally, training CNN models is computationally demanding and requires powerful hardware and time resources. To overcome these limitations, transfer learning has emerged as a powerful solution. By leveraging pre-trained CNN models, which have been trained on large datasets for generic image recognition tasks, transfer learning reduces the need for massive labeled datasets and lowers the computational burden [14]. The idea is to use the knowledge learned via a pre-trained CNN from one task and apply it to related tasks [15]. Since it starts with a pre-trained CNN backbone and only requires fine-tuning a small portion of the model, the number of training iterations required is significantly reduced, speeding up the training process. Another advantage of transfer learning is its efficiency with a small amount of data. It is particularly useful when faced with limited data, as it leverages knowledge from large-scale datasets to improve performance on smaller datasets [16]. Another significant limitation of CNNs is their inability to effectively capture global context due to their local receptive fields [17].