Review of Aneurysms Detection Methods Focusing on Selected YOLO-Based Models

Background: Aneurysms are life-threatening vascular conditions that require early and accurate detection to prevent fatal outcomes. Methods: Advances in deep learning have demonstrated significant potential in medical image analysis, particularly for automated aneurysm detection. This review paper provides an overview of selected deep-learning approaches for aneurysm detection, with an emphasis on YOLO (You Only Look Once) models and their architectural characteristics, comparative performance, and applicability. Results: Existing YOLO-based studies are examined and compared, highlighting their strengths, limitations, dataset usage, performance metrics, and clinical relevance. To complement this overview, we include test using an annotated dataset of 1342 angiograms. Of these, 1074 angiograms were used for model training and 10% (approximately 107 angiograms) were reserved for validation; the remaining 268 were used for evaluation, with all annotations provided by a radiology specialist. Conclusions: These tests were conducted solely to provide practical examples and a limited comparative demonstration of the selected YOLO variants, rather than to constitute a full original experimental study. The findings underscore the potential of integrating detection models to improve accuracy and robustness in aneurysm identification, paving the way for more reliable and validated computer-aided diagnostic systems.