TY - JOUR
T1 - Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization
AU - Chen, Yi
AU - Yang, Ming
AU - Chen, Xianqing
AU - Liu, Bin
AU - Wang, Hainan
AU - Wang, Shuihua
N1 - Publisher Copyright:
© 2016, Springer Science+Business Media New York.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - In the past, scholars used various computer vision and artificial intelligence methods to detect brain diseases via magnetic resonance imaging (MRI). In this paper, we proposed a novel system to detect sensorineural hearing loss (SNHL). First, we used three-level bior4.4 wavelet to decompose original brain image. Second, principal component analysis (PCA) was utilized for dimensionality reduction. Third, the generalized eigenvalue proximal support vector machine (GEPSVM) with Tikhonov regularization was employed as the classifier. The 10 repetitions of five-fold cross validation showed our method achieved an overall accuracy of 95.71 %. Our sensitivities over healthy control, left-sided SNHL, and right-sided SNHL are 96.00 %, 95.33 %, and 95.71 %, respectively. The proposed system is promising and effective in SNHL detection. It gives better performance than four state-of-the-art methods.
AB - In the past, scholars used various computer vision and artificial intelligence methods to detect brain diseases via magnetic resonance imaging (MRI). In this paper, we proposed a novel system to detect sensorineural hearing loss (SNHL). First, we used three-level bior4.4 wavelet to decompose original brain image. Second, principal component analysis (PCA) was utilized for dimensionality reduction. Third, the generalized eigenvalue proximal support vector machine (GEPSVM) with Tikhonov regularization was employed as the classifier. The 10 repetitions of five-fold cross validation showed our method achieved an overall accuracy of 95.71 %. Our sensitivities over healthy control, left-sided SNHL, and right-sided SNHL are 96.00 %, 95.33 %, and 95.71 %, respectively. The proposed system is promising and effective in SNHL detection. It gives better performance than four state-of-the-art methods.
KW - Dimensionality reduction
KW - Discrete wavelet transform
KW - Magnetic resonance imaging
KW - Sensorineural hearing loss
KW - Tikhonov regularization
UR - http://www.scopus.com/inward/record.url?scp=84992744390&partnerID=8YFLogxK
U2 - 10.1007/s11042-016-4087-6
DO - 10.1007/s11042-016-4087-6
M3 - Article
AN - SCOPUS:84992744390
SN - 1380-7501
VL - 77
SP - 3775
EP - 3793
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 3
ER -