RT journal article
T1 An explainable ensemble for diabetic retinopathy grading with a novel confidence quality factor and configurable heatmaps
A1 Civit Masot, Javier
A1 Luna Perejón, Francisco
A1 Muñoz Saavedra, Luis
A1 Rodríguez Corral, José María
A1 Domínguez Morales, Manuel Jesús
A1 Civit Balcells, Antón
A2 Ingeniería Informática
K1 Diabetic retinopathy
K1 Explainable AI (xAI)
K1 Deep learning
K1 Explainable ensemble
K1 Medical imaging
K1 ICDR
K1 Clinical decision support
AB 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.
PB Springer Nature
SN 0140-0118
YR 2026
FD 2026-02-05
LK http://hdl.handle.net/10498/38974
UL http://hdl.handle.net/10498/38974
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026