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dc.contributor.authorChen, Shih-Hsin
dc.contributor.authorWang, Chun-Wei
dc.contributor.authorTai, I-Hsin
dc.contributor.authorWeng, Ken-Pen
dc.contributor.authorChen, Yi-Hui
dc.contributor.authorHsieh, Kai-Sheng
dc.date2021-09
dc.date.accessioned2022-05-03T11:09:38Z
dc.date.available2022-05-03T11:09:38Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/13000
dc.description.abstractDoctors conventionally analyzed echocardiographic images for diagnosing congenital heart diseases (CHDs). However, this process is laborious and depends on the experience of the doctors. This study investigated the use of deep learning algorithms for the image detection of the ventricular septal defect (VSD), the most common type. Color Doppler echocardiographic images containing three types of VSDs were tested with color doppler ultrasound medical images. To the best of our knowledge, this study is the first one to solve this object detection problem by using a modified YOLOv4–DenseNet framework. Because some techniques of YOLOv4 are not suitable for echocardiographic object detection, we revised the algorithm for this problem. The results revealed that the YOLOv4–DenseNet outperformed YOLOv4, YOLOv3, YOLOv3–SPP, and YOLOv3–DenseNet in terms of metric mAP-50. The F1-score of YOLOv4-DenseNet and YOLOv3-DenseNet were better than those of others. Hence, the contribution of this study establishes the feasibility of using deep learning for echocardiographic image detection of VSD investigation and a better YOLOv4-DenseNet framework could be employed for the VSD detection.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 6, nº 7
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2958es_ES
dc.rightsopenAccesses_ES
dc.subjectventricular septal defect (VSD)es_ES
dc.subjectdoppler echocardiographic imageses_ES
dc.subjectobject detectiones_ES
dc.subjectdeep learninges_ES
dc.subjectYOLOv4es_ES
dc.subjectIJIMAIes_ES
dc.titleModified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Imageses_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2021.06.001


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