FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
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.
