Cataract Maturity Classification Using the VGG16 Deep Learning Model

Abstract

Cataract continues to be a major contributor to vision impairment worldwide, caused by gradual lens clouding that reduces clarity of sight. Accurately identifying the maturity level of cataracts is crucial in determining appropriate treatment planning and surgical intervention timing. However, the conventional diagnosis process still depends heavily on subjective visual assessment by ophthalmologists, which can lead to variability in classification results. To address this, the present study introduces an automated cataract maturity classification system using the VGG16 deep learning architecture through a transfer learning approach. The model distinguishes between immature and mature cataracts using clinical eye images that have undergone standardized preprocessing, including resizing, normalization, and augmentation, to improve learning robustness and avoid overfitting. Experimental evaluation shows that the model achieves 88 percent accuracy, with average precision, recall, and F1-score values of 0.88, demonstrating balanced classification performance for both classes. These outcomes indicate that VGG16 is capable of capturing relevant opacity progression characteristics associated with different cataract maturity levels. Future research may focus on broadening the dataset to include additional maturity categories, integrating explainability methods, and exploring advanced deep learning architectures to further enhance diagnostic performance and support clinical adoption.