RT Journal Article
T1 Hyperparameter optimization of YOLO models for invasive coronary angiography lesion detection and assessment
A1 Pascual-González, Mario
A1 Jiménez-Partinen, Ariadna
A1 Palomo-Ferrer, Esteban José
A1 López-Rubio, Ezequiel
A1 Ortega-Gómez, Almudena
K1 Corazón - Angiografía
K1 Angiocardiografía
K1 Aprendizaje automático (Inteligencia artificial)
AB Coronary artery disease (CAD) remains the leading cause of mortality, creating an urgent need for reproducible, image-based decision support. Although YOLOv8-based detectors underpin much of today’s state-of-the-art stenosis detection, their accuracy is sensitive to dozens of interacting hyperparameters. We therefore perform a systematic study of hyperparameter optimizers for YOLO-style models, pairing YOLOv8 and its Double Coordinate Attention (DCA) variant with three model-based engines: Covariance-Matrix-Adaptation Evolution Strategy (CMA-ES), Tree-structured Parzen Estimator (TPE), and a Gaussian-process sampler; and contrasting them with Random Search and the mutation-only routine that serves as the default optimizer in the ultralytics package.Optimization targets binary detection and was benchmarked using the CADICA (full sequences) and ARCADE (single key-frames) datasets, maximizing the F1-Score under stratified three-fold cross-validation within a fixed compute budget. Model-based methods consistently lift the F1-speed Pareto frontier: CMA-ES attains  on v8l, while Bayesian strategies top the medium and small backbones with  (v8m) and  (v8s). All surpass the default optimizer and yield more lesion-centric EigenCAM saliency, confirming the value of adaptive probabilistic search for tuning high-dimensional YOLO-based CAD pipelines. The complete code-base is open-source and released at https://github.com/MarioPasc/Coronary_Angiography_Detection.
PB Elsevier
YR 2025
FD 2025
LK https://hdl.handle.net/10630/39616
UL https://hdl.handle.net/10630/39616
LA eng
NO Mario Pascual-González, Ariadna Jiménez-Partinen, Esteban J. Palomo, Ezequiel López-Rubio, Almudena Ortega-Gómez, Hyperparameter optimization of YOLO models for invasive coronary angiography lesion detection and assessment, Computers in Biology and Medicine, Volume 196, Part B, 2025, 110697, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2025.110697.
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 12 abr 2026