期刊
ENTROPY
卷 18, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/e18050194
关键词
artificial neural network; fractional Fourier transform; Levenberg-Marquardt algorithm; principal component analysis; hearing loss; computer-aided diagnosis; unified time-frequency domain
资金
- Natural Science Foundation of Jiangsu Province [BK20150983]
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [BM2013006]
- Program of Natural Science Research of Jiangsu Higher Education Institutions [15KJB470010]
- Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province [BA2013058]
- Nanjing Normal University Research Foundation for Talented Scholars [2013119XGQ0061, 2014119XGQ0080]
- Open Project Program of the State Key Lab of CADAMP
- CG, Zhejiang University [A1616]
- Open Fund of Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University [93K172016K17]
- Open Fund of Key Laboratory of Statistical Information Technology and Data Mining, the State Statistics Bureau [SDL201608]
- Science and Technology Program of Changzhou City [CE20145055]
- Qing Lan Project of Jiangsu Province
In order to detect hearing loss more efficiently and accurately, this study proposed a new method based on fractional Fourier transform (FRFT). Three-dimensional volumetric magnetic resonance images were obtained from 15 patients with left-sided hearing loss (LHL), 20 healthy controls (HC), and 14 patients with right-sided hearing loss (RHL). Twenty-five FRFT spectrums were reduced by principal component analysis with thresholds of 90%, 95%, and 98%, respectively. The classifier is the single-hidden-layer feed-forward neural network (SFN) trained by the Levenberg-Marquardt algorithm. The results showed that the accuracies of all three classes are higher than 95%. In all, our method is promising and may raise interest from other researchers.
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