DiabeVision: Early Detection of Diabetic Retinopathy Using Image Processing and Deep Learning

Abstract

Diabetic retinopathy (DR) is a long-term eye disease that, if left unattended, leads to irreversible loss of vision. Visual inspection of DR stages from retinal fundus images is time consuming, subjective, and error-prone due to the fine gradations across stages. Early and proper detection is crucial in clinical settings for timely intervention. The core problem addressed within this project is the automatic staging of DR into its five stages using deep learning, resolving the problems of class imbalance, interpretability of the model, and computational effectiveness. This was achieved by using a hybrid deep learning model based on EfficientNetB3 as the base architecture, augmented with a custom Convolutional Block Attention Module (CBAM). The CBAM layer was added after the convolutional feature extractor to amplify feature maps using spatial and channel-wise attention mechanisms. The dataset was rebalanced with targeted oversampling to address class imbalance. In addition, a focal loss function was utilized to increase the penalty imposed on simple examples and focus learning on harder-to-classify samples. The model was deployed using TensorFlow and was trained in two phases: initially with frozen convolutional layers and then with fine-tuning enabled. Preprocessing involved resizing, normalization, and data augmentation to improve generalization. The model was validated using various data science performance metrics. On the test set, it gave an overall test accuracy of 77.42%, macro-ROC AUC score of 0.9414, and 84% training accuracy, demonstrating excellent multi-class classification performance. Accuracy per class was over 98% for the most severe DR stages (classes 3 and 4), confirming the robustness of the model in identifying critical cases. The results confirm again the efficiency of applying EfficientNetB3 using attention mechanisms and focal loss for medical imaging purposes.