Infant pain medical aid with SUN Saliency Map and SVM classifier approach

Author

Mansor, Muhammad Naufal ; Rejab, Mohd Nazri

Author_Institution

Sch. of Mehatronic Eng., Univ. Malaysia Perlis, Arau, Malaysia

fYear

2013

fDate

Nov. 29 2013-Dec. 1 2013

Firstpage

455

Lastpage

459

Abstract

This paper discussed the crucial demand regarding the scheme to translate the silence voice from the newborn. The infant can´t afford to express their feeling of pain by voice. Hence, we proudly present an infant pain recognition system to overcome this matter. We employed Saliency Using Natural statistics (SUN) Saliency Map as the feature extraction. We determine the condition of the infants (pain/no pain) with Support Vector Machine (SVM) Classifier. Several examples were conducted to evaluate the performance of the proposed method.

Keywords

feature extraction; image classification; medical image processing; paediatrics; statistical analysis; support vector machines; SUN saliency map; SVM classifier approach; feature extraction; infant pain medical aid; infant pain recognition system; performance evaluation; saliency using natural statistics saliency map; support vector machine classifier; Accuracy; Kernel; Pain; Pediatrics; Polynomials; Sun; Support vector machines; Infant Pain; SUN Saliency Map; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on

Conference_Location

Mindeb

Print_ISBN

978-1-4799-1506-4

Type

conf

DOI

10.1109/ICCSCE.2013.6720008

Filename

6720008