###### Abstract  
Diabetic retinopathy (DR) is a potentially blinding complication affecting individuals with diabetes, where early diagnosis and treatment are crucial to preventing vision loss. Recent advances in deep learning have shown promise in automating DR diagnosis, offering faster, more reliable, and cost-effective solutions. Our study employed convolutional neural networks (CNNs) to classify the severity of DR using retinal images from the EyePACS dataset, which includes 35,155 images categorized into five classes. Building on previous research that often classified DR into two classes, such as no DR and varying levels of DR, we found that while these studies typically used models like Inception V3, VGGNet, and ResNet, they focused on simplifying the diagnostic process by reducing the number of classes. However, our approach utilized a smaller, more flexible CNN architecture, allowing for a more detailed classification into five stages of DR. We employed various image preprocessing techniques, including grayscale conversion, background removal, and data augmentation, with our findings indicating that background removal significantly enhanced model performance, achieving a validation accuracy of 90.60%. This underscores the importance of sophisticated data preprocessing in medical imaging, and our study contributes to the ongoing development of automated DR diagnosis, potentially easing the burden on healthcare systems and improving patient outcomes. diabetic retinopathy classification; deep learning in medical imaging; convolutional neural networks (CNNs); image preprocessing techniques; automated diagnosis +
Footnote †: journal: Medical Imaging  
0000-0001-8823-3000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0  
Additionally, DR patients face increased risks of accidents such as falls, as well as mental health challenges, including depression and anxiety [5]. Advances in AI and deep learning offer new possibilities for automating DR diagnosis, providing faster, more accurate, and cost-effective tools [6]. Convolutional neural networks (CNNs) have demonstrated significant success in analyzing retinal images to detect and classify DR. Previous studies often categorized DR into broad groups, such as no DR and varying degrees of severity, to simplify the diagnostic process [7]. Models such as Inception V3, VGGNet, and ResNet have been effective in improving diagnostic accuracy by focusing on key features of the disease [8; 9]. However, a major research gap exists in the ability of current models to capture the finer nuances of DR progression, particularly in detecting early-stage disease. Many existing models rely on broad distinctions between disease stages, which can overlook subtle but critical retinal changes, such as microaneurysms and small hemorrhages. This lack of sensitivity in capturing these minor features can result in missed opportunities for early diagnosis and intervention [4; 5; 6; 7; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. In response to the limitations of existing models in detecting early-stage diabetic retinopathy (DR), our study compares the performance of three convolutional neural network (CNN) models using different image preprocessing techniques and training strategies. We assess models trained on grayscale images, standard RGB images, and RGB images with fewer training epochs. This approach focuses on how advanced preprocessing methods--such as grayscale conversion, background removal, and data augmentation--can enhance the detection of subtle retinal features crucial for early DR diagnosis. By systematically evaluating these techniques, we aim to address unresolved issues in current research, including the challenges of early-stage detection, inconsistent performance across various image types, and the balance between computational efficiency and diagnostic accuracy. Examining the effects of reduced training epochs also offers insights into optimizing computational resources without sacrificing model performance.