Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1697
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ervural, Saim | - |
dc.contributor.author | Ceylan, Murat | - |
dc.date.accessioned | 2022-01-30T17:32:54Z | - |
dc.date.available | 2022-01-30T17:32:54Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1768-6733 | - |
dc.identifier.issn | 2116-7176 | - |
dc.identifier.uri | https://doi.org/10.1080/17686733.2021.2010379 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1697 | - |
dc.description.abstract | Monitoring the body temperatures and evaluating the thermal asymmetry of newborns give an idea about neonatal diseases. Infrared thermography is a non-invasive, non-harmful, and non-contact modality that allows the monitoring of the body temperature distribution. Early diagnosis using a limited data set is extremely vital due to the high mortality rate in newborns and some difficulties in neonatal imaging. Thermography stands out as a useful tool in detecting neonatal diseases compared to other techniques. However, creating a thermogram database consisting of thousands of images from each class required by traditional artificial intelligence methods, is impossible due to the sensitivity of newborns. One of the meta-learning models that has recently gained success in applying limited data learning, especially one-shot, in various fields is Siamese neural networks. In this work, we perform a multi-class classification to provide pre-diagnosis to experts in disease detection using Siamese neural networks. By using two different optimisation techniques and data augmentation, critical diseases with only a few sample data are classified using the method tested in two- and three-class evaluation approaches. The results based on the disease type achieve 99.4% accuracy in infection diseases and 96.4% oesophageal atresia, 97.4% in intestinal atresia, and 94.02% in necrotising enterocolitis. | en_US |
dc.description.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [215E019] | en_US |
dc.description.sponsorship | This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, project number: 215E019). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis Ltd | en_US |
dc.relation.ispartof | Quantitative Infrared Thermography Journal | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Disease Classification | en_US |
dc.subject | Neonatal Intensive Care | en_US |
dc.subject | One-Shot Learning | en_US |
dc.subject | Siamese Neural Network | en_US |
dc.subject | Thermal Imaging | en_US |
dc.subject | Low-Birth-Weight | en_US |
dc.subject | Infrared Thermography | en_US |
dc.subject | Natural-History | en_US |
dc.subject | System | en_US |
dc.subject | Thermoregulation | en_US |
dc.subject | Anomalies | en_US |
dc.subject | Infants | en_US |
dc.title | Thermogram classification using deep siamese network for neonatal disease detection with limited data | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1080/17686733.2021.2010379 | - |
dc.identifier.scopus | 2-s2.0-85122016157 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.wos | WOS:000736036900001 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57195215988 | - |
dc.authorscopusid | 56276648900 | - |
dc.identifier.scopusquality | Q3 | - |
item.fulltext | With Fulltext | - |
item.openairetype | Article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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File | Size | Format | |
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Thermogram classification using deep siamese network for neonatal disease detection with limited data.pdf Until 2030-01-01 | 3.32 MB | Adobe PDF | View/Open Request a copy |
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