EfficientNetB0-Based End-to-End Diagnostic System for Diabetic Retinopathy Grading and Macular Edema Detection.

Purpose: This study aims to develop and validate a deep learning-based automated diagnostic system that utilizes fluorescein angiography (FFA) images for the rapid and accurate diagnosis of diabetic retinopathy (DR) and its complications.

Methods: We collected 19,031 FFA images from 2753 patients between June 2017 and March 2024 to construct and evaluate our analytical framework. The images were preprocessed and annotated for training and validating the deep learning model. The study employed a two-stage deep learning system: the first stage used EfficientNetB0 for a five-class classification task to differentiate between normal retinal conditions, various stages of DR, and post-laser treatment status; the second stage focused on images classified as abnormal in the first stage, further detecting the presence of diabetic macular edema (DME). Model performance was evaluated using multiple classification metrics, including accuracy, AUC, precision, recall, F1-score, and Cohen's kappa coefficient.

Results: In the first stage, the model achieved an accuracy of 0.7036 and an AUC of 0.9062 on the test set, demonstrating high accuracy and discriminative ability. In the second stage, the model achieved an accuracy of 0.7258 and an AUC of 0.7530, performing well. Additionally, through Grad-CAM (gradient-weighted class activation mapping), we visualized the most influential image regions in the model's decision-making process, enhancing the model's interpretability.

Conclusion: This study successfully developed an end-to-end DR diagnostic system based on the EfficientNetB0 model. The system not only automates the grading of FFA images but also detects DME, significantly reducing the time required for image interpretation by clinicians and providing an effective tool to improve the efficiency and accuracy of DR diagnosis.