Hearing Loss Detection in Medical Multimedia Data by Discrete Wavelet Packet Entropy and Single-Hidden Layer Neural Network Trained by Adaptive Learning-Rate Back Propagation

被引:8
|
作者
Wang, Shuihua [1 ]
Du, Sidan [2 ]
Li, Yang [2 ]
Lu, Huimin [3 ]
Yang, Ming [4 ]
Liu, Bin [5 ]
Zhang, Yudong [1 ,6 ]
机构
[1] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
[2] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210046, Jiangsu, Peoples R China
[3] Kyushu Inst Technol, Dept Mech & Control Engn, Kitakyushu, Fukuoka 8048550, Japan
[4] Nanjing Med Univ, Dept Radiol, Childrens Hosp, Nanjing 210008, Peoples R China
[5] Southeast Univ, Dept Radiol, Zhong Da Hosp, Nanjing 210009, Peoples R China
[6] Columbia Univ, Translat Imaging Div, New York, NY 10032 USA
来源
关键词
Hearing loss; Multimedia data; Discrete wavelet packet entropy; Single-hidden layer neural network; PATHOLOGICAL BRAIN DETECTION; IMAGE CLASSIFICATION; DECISION TREE; TRANSFORM; PERFORMANCE; SELECTION; MACHINE;
D O I
10.1007/978-3-319-59081-3_63
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In order to develop an efficient computer-aided diagnosis system for detecting left-sided and right-sided sensorineural hearing loss, we used artificial intelligence in this study. First, 49 subjects were enrolled by magnetic resonance imaging scans. Second, the discrete wavelet packet entropy (DWPE) was utilized to extract global texture features from brain images. Third, single-hidden layer neural network (SLNN) was used as the classifier with training algorithm of adaptive learning-rate back propagation (ALBP). The 10 times of 5-fold cross validation demonstrated our proposed method yielded an overall accuracy of 95.31%, higher than standard back propagation method with accuracy of 87.14%. Besides, our method also outperforms the "FRFT + PCA (Yang, 2016)", "WE + DT (Kale, 2013)", and "WE + MRF (Vasta 2016)". In closing, our method is efficient.
引用
收藏
页码:541 / 549
页数:9
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