Stage Aware Diagnosis of Diabetic Retinopathy via Ordinal Regression
This work addresses timely screening for diabetic retinopathy to prevent blindness, but it appears incremental as it applies an existing method to a specific dataset with preprocessing.
The paper tackled the problem of diagnosing diabetic retinopathy stages using an ordinal regression framework on the APTOS-2019 dataset, achieving a Quadratic Weighted Kappa score of 0.8992, which sets a new benchmark.
Diabetic Retinopathy (DR) has emerged as a major cause of preventable blindness in recent times. With timely screening and intervention, the condition can be prevented from causing irreversible damage. The work introduces a state-of-the-art Ordinal Regression-based DR Detection framework that uses the APTOS-2019 fundus image dataset. A widely accepted combination of preprocessing methods: Green Channel (GC) Extraction, Noise Masking, and CLAHE, was used to isolate the most relevant features for DR classification. Model performance was evaluated using the Quadratic Weighted Kappa, with a focus on agreement between results and clinical grading. Our Ordinal Regression approach attained a QWK score of 0.8992, setting a new benchmark on the APTOS dataset.