@misc{10498/38974,
year = {2026},
month = {2},
url = {http://hdl.handle.net/10498/38974},
abstract = {While current artificial intelligence (AI) tools aid in detecting diabetic retinopathy (DR), they face significant challenges that limit their clinical utility. Most are restricted to binary (referable vs. non-referable) screening and operate as “black boxes,” lacking the detailed, transparent explanations required for diagnostic confidence. This study addresses these gaps by introducing a novel, explainable ensemble-based approach for detailed DR grading. Our system utilizes a parallel ensemble of two efficient deep learning networks, EfficientNetV2 and ConvNeXt, to perform a full five-class international clinical diabetic retinopathy (ICDR) classification. The proposed model achieves state-of-the-art performance, with 96.7% accuracy and an Area Under the Curve (AUC) over 96% for all classes on a public dataset. More importantly, it provides a comprehensive diagnostic report designed to enhance clinical trust and utility. This report features multiple, configurable superimposed heatmaps, two probability-ordered diagnostic suggestions, and a novel quality factor that estimates the confidence of the prediction. By offering richer, more transparent, and interactive explanations, our system moves beyond simple screening to function as a valuable diagnostic assistance tool for ophthalmologists and other healthcare professionals.},
publisher = {Springer Nature},
keywords = {Diabetic retinopathy},
keywords = {Explainable AI (xAI)},
keywords = {Deep learning},
keywords = {Explainable ensemble},
keywords = {Medical imaging},
keywords = {ICDR},
keywords = {Clinical decision support},
title = {An explainable ensemble for diabetic retinopathy grading with a novel confidence quality factor and configurable heatmaps},
doi = {10.1007/s11517-026-03514-2},
author = {Civit Masot, Javier and Luna Perejón, Francisco and Muñoz Saavedra, Luis and Rodríguez Corral, José María and Domínguez Morales, Manuel Jesús and Civit Balcells, Antón},
}