Comparison of Linear Discriminant Analysis, Support Vector Machines and Naive Bayes Methods in the Classification of Neonatal Hyperspectral Signatures

Cihan, MücahitCeylan, Murat2021-12-132021-12-1320219781665436496https://doi.org/10.1109/SIU53274.2021.9477861https://hdl.handle.net/20.500.13091/37129th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- -- 170536Hyperspectral 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.trinfo:eu-repo/semantics/closedAccessClassificationHyperspectral imagingMachine learning methodsNeonatal spectral signatureComparison of Linear Discriminant Analysis, Support Vector Machines and Naive Bayes Methods in the Classification of Neonatal Hyperspectral SignaturesYenido?an Hiperspektral İmzalarinin Siniflandirilmasinda Lineer Diskriminant Analizi, Destek Vektör Makineleri ve Naive Bayes Yöntemlerinin KarşilaştirilmasiConference Object10.1109/SIU53274.2021.94778612-s2.0-85111451769