期刊
INTEGRATED COMPUTER-AIDED ENGINEERING
卷 26, 期 4, 页码 411-426出版社
IOS PRESS
DOI: 10.3233/ICA-190605
关键词
Unilateral sensorineural hearing loss; dual-tree complex wavelet transform; kernel principal component analysis; multinomial logistic regression; double-density dual-tree complex wavelet transform; magnetic resonance imaging; alexNet
类别
资金
- Natural Science Foundation of China [61602250]
- Natural Science Foundation of Jiangsu Province [BK20150983, BK20150982]
- Program of Natural Science Research of Jiangsu Higher Education Institutions [16KJB520025]
- MIN ECO [TEC2015-64718-R]
- Salvador de Madariaga Mobility Grants 2017
- Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia, Spain) [P11-TIC-7103]
AIM: Unilateral sensorineural hearing loss is a brain disease, which causes slight morphology changes within brain structure. Traditional manual method may ignore this change. METHOD: In this work, we developed a novel method, based on the double-density dual-tree complex (DDDTCWT), and radial basis function kernel principal component analysis (RKPCA) and multinomial logistic regression (MLR) for the magnetic resonance imaging scanning. We first used DDDTCWT to extract features. Afterwards, we used RKPCA to reduce feature dimensionalities. Finally, MLR was employed to be the classifier. RESULT: The 10 times of 10-fold stratified cross validation showed our method achieved an overall accuracy of 96.44 +/- 0.88%. The sensitivities of detecting left-sided sensorineural hearing loss, right-sided sensorineural hearing loss, and healthy controls were 96.67 +/- 2.72%, 96.67 +/- 3.51%, and 96.00 +/- 4.10%, respectively. CONCLUSION: Our method performed better than both raw and improved AlexNet, and eight state-of-the-art methods via a stringent statistical 10 x 10-fold stratified cross validation. The MLR gives better classification performance than decision tree, support vector machine, and back-propagation neural network.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据