Leveraging Innovative CNN Architectures for Diabetic Retinopathy Detection

One of the main causes of vision impairment worldwide is still diabetic retinopathy (DR), which emphasizes the necessity for accurate diagnosis techniques. With their potential to automate DR detection, Convolutional Neural Networks (CNNs) hold great promise for early diagnosis and better treatment outcomes. Advanced CNN architectures such as VGG16, VGG19, ResNet, Inception, DenseNet, MobileNet, Xception, and SqueezeNet are investigated in this research. These architectures have different properties, ranging from lightweight designs appropriate for mobile deployment to deep layer stacks. Researchers hope to improve the DR screening systems' practicality, efficiency, and accuracy by utilizing these architectures. The study aims to provide light on how these CNNs can improve DR detection, thereby enabling early intervention and bettering patient outcomes, through in-depth examination of architectural design, training techniques, and performance.