A novel combined deep learning methodology to non-invasively estimate hemoglobin levels in blood with high accuracy
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Hemoglobin 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.
Açıklama
Anahtar Kelimeler
Combined deep learning, Hemoglobin estimation, Neural network, Non-invasive
Kaynak
Medical Engineering & Physics
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
108
