A novel combined deep learning methodology to non-invasively estimate hemoglobin levels in blood with high accuracy

Basit Öğe Kaydı
dc.contributor.authorYilmaz, Hakan
dc.contributor.authorKizilates, Burcu S.
dc.contributor.authorShaaban, Fatema
dc.contributor.authorKaratas, Ziya R.
dc.date.accessioned2024-09-29T15:57:56Z
dc.date.available2024-09-29T15:57:56Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractHemoglobin is an essential protein found in blood and should not fall below a certain level in humans. Today's methods of hemoglobin measurement are mostly invasive. This study aims to perform a non-invasive estimation of hemoglobin levels using age, height, weight, body mass index, gender, and nail images of individuals. Data was collected from 353 volunteers aged 1 to 92 years. Two different data sets were created using these data: a numerical dataset and a nail image set. A combined deep learning model was put forward using both the model created for numerical data and the model created for nail images. In this study, bias was calculated as 0.03 g/dL, and the limits of agreement value in the 95% confidence interval was calculated as 1.09 g/dL. The calculated mean absolute percentage error values were 2.09%, and the root mean squared error was 0.56 g/dL. After entering the necessary data into the system, the estimated average resulting time was 0.09 s. The results of this study have shown success compared to the results of similar studies, and this method can be used for non-invasive hemoglobin level estimation. The recommended approach is more comfortable than invasive methods and gives much faster results.en_US
dc.identifier.doi10.1016/j.medengphy.2022.103891
dc.identifier.issn1350-4533
dc.identifier.issn1873-4030
dc.identifier.pmid36195369en_US
dc.identifier.scopus2-s2.0-85138449752en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1016/j.medengphy.2022.103891
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5110
dc.identifier.volume108en_US
dc.identifier.wosWOS:000870353800001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMedical Engineering & Physicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCombined deep learningen_US
dc.subjectHemoglobin estimationen_US
dc.subjectNeural networken_US
dc.subjectNon-invasiveen_US
dc.titleA novel combined deep learning methodology to non-invasively estimate hemoglobin levels in blood with high accuracyen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu