Diabetic retinopathy (DR) is a prevalent complication of diabetes that can lead to vision impairment if not diagnosed and treated promptly. This study presents a novel approach for the automated grading of diabetic retinopathy in ultra-widefield fundus images (UFI) using deep learning techniques. We propose a method that involves preprocessing UFIs by cropping the central region to focus on the most relevant information. Subsequently, we employ state-of-the-art deep learning models, including ResNet50, EfficientNetB3, and Xception, to perform DR grade classification. Our extensive experiments reveal that Xception outperforms the other models in terms of classification accuracy, sensitivity, and specificity. his research contributes to the development of automated tools that can assist healthcare professionals in early DR detection and management, thereby reducing the risk of vision loss among diabetic patients.
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Acknowledgement
This work was supported in part by the BK21 FOUR project, in part by IITP grant funded by the Korean government (MSIT) under the ICT Creative Consilience program (IITP-2023-2020-0-01821), Artificial Intelligence Innovation Hub (IITP-2021-0-02068), and AI Graduate School program (IITP-2019-0-00421)