TY  - JOUR
TI  - Classification of neonatal jaundice in mobile application with noninvasive image
processing methods
AB  - This study aims a mobile support system to aid health care professionals in hospitals or in regions far away
from hospitals to utilize noninvasive image processing methods for classification of neonatal jaundice. A considerably low
processing cost is aimed to be attained by developing an algorithm that could work on a mobile device with low-end camera
and processor capabilities within this study. In this context, an algorithm with low cost is developed performing detection
of most meaningful parameters by a multiple input single output regression model and correlation.The advantage of the
proposed method is that it can estimate bilirubin with the help of a simple regression curve. The reason for its low cost
is that the noninvasive jaundice prediction is performed with a simple regression curve instead of many mathematical
operations in morphological image processing methods. The study was performed on a total of 196 subjects, 61 of which
were classified as severe jaundice while 95 of the newborns were mild jaundice cases, and other 40 cases are used for
tests. As a result of this work, the two-group classification accuracy of the developed algorithm is observed to be 92.5%
for the 40 subject test group.
AU  - AKYEL, Anıl
AU  - KUTBAY, Uğurhan
AU  - Hardalaç, Fırat
AU  - HAİ, Bo
AU  - AYTURAN, Kubilay
AU  - Aydin, Mustafa
AU  - MERT, Fatih
AU  - ÇAĞLAR, Atika
DO  - 10.3906/elk-2008-76
PY  - 2021
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 29
IS  - 4
SN  - 1300-0632
SP  - 2116
EP  - 2126
DB  - TRDizin
UR  - http://search/yayin/detay/523904
ER  -