FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
This work addresses the problem of inconsistent fundus image quality assessment for clinical screening applications, though it is incremental as it builds on existing deep learning methods with a new structured framework.
The paper tackled the challenge of automated fundus image quality assessment by introducing FundaQ-8, a clinically-inspired scoring framework, and developed a ResNet18-based model that predicts quality scores, validated on real-world datasets to improve diagnostic robustness in diabetic retinopathy grading.
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.