دورية أكاديمية
Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning
| العنوان: | Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning |
|---|---|
| المؤلفون: | Weber, Raphael, Cabon, Sandie, Simon, Antoine, Poree, Fabienne, Carrault, Guy |
| المساهمون: | Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), 689260, European Unions Horizon 2020 research and innovation programme |
| المصدر: | ISSN: 2168-2194 ; IEEE Journal of Biomedical and Health Informatics ; https://hal.science/hal-03229053Test ; IEEE Journal of Biomedical and Health Informatics, 2021, 25 (5), pp.1419-1428. ⟨10.1109/JBHI.2021.3062617⟩. |
| بيانات النشر: | HAL CCSD Institute of Electrical and Electronics Engineers |
| سنة النشر: | 2021 |
| المجموعة: | Université de Rennes 1: Publications scientifiques (HAL) |
| مصطلحات موضوعية: | [SDV.IB]Life Sciences [q-bio]/Bioengineering |
| الوصف: | International audience ; Video-based motion analysis recently appeared to be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and noninvasive. However it is important to remove periods when the newborn is absent or an adult is present from the analysis. In this paper, we propose a method for automatic detection of preterm newborn presence in incubator and open bed. We learn a specific model for each bed type as the camera placement differs a lot and the encountered situations are different between both. We break the problem down into two binary classifications based on deep transfer learning that are fused afterwards: newborn presence detection on the one hand and adult presence detection on the other hand. Moreover, we adopt a strategy of decision intervals fusion in order to take advantage of temporal consistency. We test three deep neural network that were pre-trained on ImageNet: VGG16, MobileNetV2 and InceptionV3. Two classifiers are compared: support vector machine and a small neural network. Our experiments are conducted on a database of 120 newborns. The whole method is evaluated on a subset of 25 newborns including 66 days of video recordings. In incubator, we reach a balanced accuracy of 86%. In open bed, the performance is lower because of a much wider variety of situations whereas less data are available. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/pmid/33646962; hal-03229053; https://hal.science/hal-03229053Test; https://hal.science/hal-03229053/documentTest; https://hal.science/hal-03229053/file/Weber%20et%20al-2021-Preterm%20newborn%20presence%20detection%20in%20incubator%20and%20open%20bed%20using%20deep.pdfTest; PUBMED: 33646962 |
| DOI: | 10.1109/JBHI.2021.3062617 |
| الإتاحة: | https://doi.org/10.1109/JBHI.2021.3062617Test https://hal.science/hal-03229053Test https://hal.science/hal-03229053/documentTest https://hal.science/hal-03229053/file/Weber%20et%20al-2021-Preterm%20newborn%20presence%20detection%20in%20incubator%20and%20open%20bed%20using%20deep.pdfTest |
| حقوق: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.B9088221 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/JBHI.2021.3062617 |
|---|