FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US

dc.authorid0000-0002-4370-7474
dc.authorid0000-0001-6559-1399
dc.contributor.authorTurkan, Musa
dc.contributor.authorDandıl, Emre
dc.contributor.authorUrfalı, Furkan Ertürk
dc.contributor.authorKorkmaz, Mehmet
dc.date.accessioned2025-04-28T14:06:49Z
dc.date.available2025-04-28T14:06:49Z
dc.date.issued2025en_US
dc.departmentEnstitüler, Fen Bilimleri Enstitüsü, Elektronik ve Bilgisayar Mühendisliği
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAutomated classification of fetal movements in ultrasound (US) videos is critical for assessing fetal well-being and detecting potential complications during pregnancy. This study introduces FetalMovNet, a novel deep learning model that incorporates an attention mechanism to improve the classification of fetal movement in US video sequences. The model integrates convolutional neural networks (CNN) for feature extraction and an attention mechanism to capture spatio-temporal patterns, significantly improving classification performance of fetal movements. To evaluate FetalMovNet, we construct a new dataset containing fetal movements in US across seven different anatomical structures-head, body, arm, hand, heart,leg, and foot. Experimental results show that FetalMovNet achieves an accuracy of 0.9887, precision of 0.9871, recall of 0.9910, and an F1-score of 0.9891, outperforming state-of-the-art CNN and CNN-LSTM architectures. Ablation studies confirm the effectiveness of the attention mechanism, with FetalMovNet achieving an area under curve (AUC) score of 0.9957, compared to 0.9471 for CNN and 0.9543 for CNNLSTM.The proposed FetalMovNet model provides a robust and clinically applicable tool for real-time fetal movement monitoring, reducing the need for manual assessment and improving prenatal care.en_US
dc.identifier.citationTurkan, M., Dandil, E., Urfali, F. E., & Korkmaz, M. (2025). FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US. IEEE Access.en_US
dc.identifier.doi10.1109/ACCESS.2025.3553548
dc.identifier.endpage52527en_US
dc.identifier.scopus2-s2.0-105001558427
dc.identifier.scopusqualityQ1
dc.identifier.startpage52508en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3553548
dc.identifier.urihttps://hdl.handle.net/11552/3928
dc.identifier.volume13en_US
dc.identifier.wosWOS:001455525600024
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWoS
dc.indekslendigikaynakWoS - Science Citation Index Expanded
dc.indekslendigikaynakScopus
dc.institutionauthorTurkan, Musa
dc.institutionauthorDandıl, Emre
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectFetusen_US
dc.subjectFetal Movement Detectionen_US
dc.subjectUS Videoen_US
dc.subjectDeep Learningen_US
dc.subjectCNNen_US
dc.subjectAttention Mechanismen_US
dc.titleFetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US
dc.typeArticle

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