FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913912093409280 |
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| author | Zun, Lee Qi Hao, Oscar Wong Jin Omar, Nor Anita Binti Che Asnir, Zalifa Zakiah Binti Zainal, Mohamad Sabri bin Sinal Fye, Goh Man |
| author_facet | Zun, Lee Qi Hao, Oscar Wong Jin Omar, Nor Anita Binti Che Asnir, Zalifa Zakiah Binti Zainal, Mohamad Sabri bin Sinal Fye, Goh Man |
| contents | Automated fundus image quality assessment (FIQA) remains a challenge due to variations in image acquisition and subjective expert evaluations. We introduce FundaQ-8, a novel expert-validated framework for systematically assessing fundus image quality using eight critical parameters, including field coverage, anatomical visibility, illumination, and image artifacts. Using FundaQ-8 as a structured scoring reference, we develop a ResNet18-based regression model to predict continuous quality scores in the 0 to 1 range. The model is trained on 1800 fundus images from real-world clinical sources and Kaggle datasets, using transfer learning, mean squared error optimization, and standardized preprocessing. Validation against the EyeQ dataset and statistical analyses confirm the framework's reliability and clinical interpretability. Incorporating FundaQ-8 into deep learning models for diabetic retinopathy grading also improves diagnostic robustness, highlighting the value of quality-aware training in real-world screening applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20303 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment Zun, Lee Qi Hao, Oscar Wong Jin Omar, Nor Anita Binti Che Asnir, Zalifa Zakiah Binti Zainal, Mohamad Sabri bin Sinal Fye, Goh Man Image and Video Processing Computation and Language Computer Vision and Pattern Recognition Automated fundus image quality assessment (FIQA) remains a challenge due to variations in image acquisition and subjective expert evaluations. We introduce FundaQ-8, a novel expert-validated framework for systematically assessing fundus image quality using eight critical parameters, including field coverage, anatomical visibility, illumination, and image artifacts. Using FundaQ-8 as a structured scoring reference, we develop a ResNet18-based regression model to predict continuous quality scores in the 0 to 1 range. The model is trained on 1800 fundus images from real-world clinical sources and Kaggle datasets, using transfer learning, mean squared error optimization, and standardized preprocessing. Validation against the EyeQ dataset and statistical analyses confirm the framework's reliability and clinical interpretability. Incorporating FundaQ-8 into deep learning models for diabetic retinopathy grading also improves diagnostic robustness, highlighting the value of quality-aware training in real-world screening applications. |
| title | FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment |
| topic | Image and Video Processing Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.20303 |