Comparative Evaluations of Deep Learning Models for Diabetic Retinopathy Classification

This study aims to find the best fit algorithm for well-known disease Retinopathy caused by diabetes through a Deep Learning approach on two datasets. Methods: Four Deep Learning methods are employed on the datasets of Diabetic Retinopathy. The methods applied on the grayscale image files. Here, the image files from the dataset are divided into three parts: training- 80%, validation test -10% and test data-10%. We use the following preprocessing techniques to improve model generalization and address issues caused by sparse and unbalanced datasets: resize (aspect ratio), crop, normalize, augment (rotate,zoom,flipping). These techniques replicate realistic variations in retinal images and lessen overfitting. Our goal is to have clean, limited noise and test data to evaluate the model accurately. The dataset has been trained using the following methods: VGG16, ResNet50, InceptionV3 and EfficientNet correspondingly. Findings: From the above methods on the dataset-1, accuracy found from ResNet50 stood highest and on the dataset-2, accuracy found from Inception-v3 stood highest among the others. Novelty: To test generalizability and robustness, this study compares four significant convolutional neural network architectures for diabetic retinopathy classification across two heterogeneous retinal imaging datasets. Our work explores crossdataset performance, model efficiency and clinically important metrics, providing useful insights for real-world deployment, in contrast to previous studies that were restricted to single-dataset evaluations.