Introduction of hierarchical deep features based on the VGG-16 model for enhancing diabetic retinopathy...

Diabetic Retinopathy (DR) is a common ocular disease that presents a significant risk of vision loss in individuals with diabetes. Accurate DR classification is critical for preventing disease progression and preserving patients' vision. However, DR classification is often complex due to the high degree of similarity and overlap among features across its different stages. To address these challenges, this study introduces a computer-aided diagnosis framework that leverages deep neural networks to extract hierarchical features. In this framework, a hierarchical semantic resolution pyramid of retinal images is generated by integrating feature maps from the pooling layers of a deep neural network model. Handcrafted features are subsequently extracted from the HSRP and then fused to create a comprehensive feature vector. This approach fuses pre-trained neural networks with handcrafted features to effectively identify distinctive image characteristics. Notably, due to the independence of feature maps from input data, the proposed architecture does not require retraining or fine-tuning, enhancing its generalizability across different image domains. To identify the most effective discriminative features for classifying DR stages, the Boruta-Shap algorithm is applied to the feature vector. With the high discriminative power of the selected features and the limited dataset size, a random forest classifier is used to categorize DR into five stages. Comparative performance analysis with existing methods demonstrates the effectiveness of the proposed approach in overcoming the challenges associated with DR classification.