Scopus
🔓 Açık Erişim
Classification of medical thermograms belonging neonates by using segmentation, feature engineering and machine learning algorithms
Traitement du Signal · Ekim 2020
Makale Bilgileri
DergiTraitement du Signal
Yayın TarihiEkim 2020
Cilt / Sayfa37 · 611-617
Scopus ID2-s2.0-85096507594
Erişim🔓 Açık Erişim
Özet
Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.
Yazarlar (6)
1
Ahmet H. Ornek
2
Saim Ervural
3
Murat Ceylan
4
Murat Konak
5
Hanifi Soylu
6
Duygu Savasci
Anahtar Kelimeler
Fast correlation-based filter
Local binary pattern
Machine learning
Neonate
Thermography
Kurumlar
Karatay Üniversitesi
Konya Turkey
Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
5
Atıf
6
Yazar
5
Anahtar Kelime