Hyperparameter optimization of YOLO models for invasive coronary angiography lesion detection and assessment

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 0.355±0.079 on v8l, while Bayesian strategies top the medium and small backbones with 0.346±0.048 (v8m) and 0.304±0.054 (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.