Benchmarking YOLO Models for Intracranial Hemorrhage Detection Using Varied CT Data Sources

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dc.catalogadorjwg
dc.contributor.authorTapia, Gonzalo
dc.contributor.authorAllende-Cid, Hector
dc.contributor.authorChabert, Steren
dc.contributor.authorMery Quiroz Domingo Arturo
dc.contributor.authorSalas, Rodrigo
dc.date.accessioned2025-03-18T18:57:19Z
dc.date.available2025-03-18T18:57:19Z
dc.date.issued2024
dc.description.abstractIntracranial hemorrhages (ICH) are a significant challenge in emergency medicine due to the critical nature of a timely and accurate diagnosis. This study evaluates the performance of six versions of the You Only Look Once (YOLO) object detection model, from YOLOv5 to YOLOv10, in detecting ICH using computed tomography (CT) scans. The primary focus is understanding the advancements in YOLO architectures over time and their impact on detection accuracy and inference speed. The study used the Brain Hemorrhage Extended Dataset (BHX), comprising 491 CT scans with annotations for six types of hemorrhages: epidural, subdural, subarachnoid, intraparenchymal, intraventricular, and chronic hemorrhage, and introduces a new data set obtained from a major hospital in Chile. The models were trained using a combination of single-class and multi-class approaches to address class imbalance and were evaluated based on precision, recall, F1 score, and mean average precision (mAP). The models were evaluated in three distinct contexts: 1) a biased scenario where images of the same individual could appear in both training and testing sets, 2) a cross-validation setup ensuring the independence of images by separating the sets based on subjects, and 3) an external validation using one dataset for training and the Chilean dataset for testing, maintaining full independence between training and evaluation. The findings indicate that YOLOv8 and YOLOv10 demonstrate superior detection accuracy and inference efficiency performance, respectively, compared to previous versions. In particular, with image independence, YOLOv8 reached the highest average mAP for all classes, with a score of 0.4. This comparative analysis provides information on the effectiveness of architectural advances in YOLO models for medical applications and suggests directions for future improvements in ICH detection.
dc.fuente.origenORCID
dc.identifier.doi10.1109/ACCESS.2024.3510517
dc.identifier.issn2169-3536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3510517
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102765
dc.identifier.wosidWOS:001380709600010
dc.information.autorucEscuela de Ingeniería; Mery Quiroz Domingo Arturo; 0000-0003-4748-3882; 102382
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final188101
dc.pagina.inicio188084
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.revistaIEEE ACCESS
dc.rightsacceso restringido
dc.subjectYOLO
dc.subjectHemorrhaging
dc.subjectComputed tomography
dc.subjectPredictive models
dc.subjectAccuracy
dc.subjectTraining
dc.subjectSolid modeling
dc.subjectData models
dc.subjectConvolutional neural networks
dc.subjectMicroprocessors
dc.subjectComputed assisted diagnosis (CAD)
dc.subjectconvolutional neural networks (CNN)
dc.subjectmedical imaging
dc.subjectintracranial hemorrhage detection
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleBenchmarking YOLO Models for Intracranial Hemorrhage Detection Using Varied CT Data Sources
dc.typeartículo
dc.volumen12
sipa.codpersvinculados102382
sipa.trazabilidadORCID;2025-03-03
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