Abstract
Introduction Diabetic retinopathy (DR) is a leading cause of blindness globally, emphasizing the urgent need for efficient diagnostic tools. Machine learning, particularly convolutional neural networks (CNNs), has shown promise in automating the diagnosis of retinal conditions with high accuracy. This study evaluates two CNN models, VGG16 and InceptionV3, for classifying retinal optical coherence tomography (OCT) images into four categories: normal, choroidal neovascularization, diabetic macular edema (DME), and drusen. Methods Using 83,000 OCT images across four categories, the CNNs were trained and tested via Python-based libraries, including TensorFlow and Keras. Metrics such as accuracy, sensitivity, and specificity were analyzed with confusion matrices and performance graphs. Comparisons of dataset sizes evaluated the impact on model accuracy with tools deployed on JupyterLab. Results VGG16 and InceptionV3 achieved accuracy between 85% and 95%, with VGG16 peaking at 94% and outperforming InceptionV3 (92%). Larger datasets improved sensitivity by 7% and accuracy across all categories, with the highest performance for normal and drusen classifications. Metrics like sensitivity and specificity positively correlated with dataset size. Conclusions The study confirms CNNs' potential in retinal diagnostics, achieving high classification accuracy. Limitations included reliance on grayscale images and computational intensity, which hindered finer distinctions. Future work should integrate data augmentation, patient-specific variables, and lightweight architectures to optimize performance for clinical use, reducing costs and improving outcomes.