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Title: Comparison of linear discriminant analysis, support vector machines and naive bayes methods in the classification of neonatal hyperspectral signatures
Other Titles: Yenido?an hiperspektral imzalarinin siniflandirilmasinda lineer diskriminant analizi, destek vektör makineleri ve naive bayes yöntemlerinin karşilaştirilmasi
Authors: Cihan, Mücahit
Ceylan, Murat
Keywords: Classification
Hyperspectral imaging
Machine learning methods
Neonatal spectral signature
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Hyperspectral imaging (HSI) is an imaging method that enables to obtain a large number of two-dimensional images in a wide wavelength range in the electromagnetic spectrum band. HSI has an important potential in biomedical applications as it provides diagnostic information about tissue physiology, morphology and composition. In this study, spectral signatures of unhealthy and healthy neonates were extracted using HSI method. Then, data statistics (minimum, maximum, mean, median and standard deviation) of these signatures were obtained and classified with Linear Discriminant Analysis, Support Vector Machines and Naive Bayes Methods. After the classification processes, the best result was achieved using Linear Discriminant Analysis with 90.63% accuracy, 87.50% sensitivity and 93.75% specificity. © 2021 IEEE.
Description: 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- -- 170536
ISBN: 9781665436496
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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