Stage Aware Diagnosis of Diabetic Retinopathy via Ordinal Regression
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911274057596928 |
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| author | Kumar, Saksham Aditya, D Sridhar Kumar, T Likhil Bikku, Thulasi Thota, Srinivasarao Kumar, Chandan |
| author_facet | Kumar, Saksham Aditya, D Sridhar Kumar, T Likhil Bikku, Thulasi Thota, Srinivasarao Kumar, Chandan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14398 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stage Aware Diagnosis of Diabetic Retinopathy via Ordinal Regression Kumar, Saksham Aditya, D Sridhar Kumar, T Likhil Bikku, Thulasi Thota, Srinivasarao Kumar, Chandan Computer Vision and Pattern Recognition 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. |
| title | Stage Aware Diagnosis of Diabetic Retinopathy via Ordinal Regression |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.14398 |