Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning.
HighlightsWe develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls.Aim:Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way.Materials:We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC).Method:We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM).Results:The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier.Conclusions:This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.
School of Electronic Science and Engineering, Nanjing UniversityNanjing, China; School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police AcademyChangsha, China.
Shuihua
S
Wang
Department of Radiology, Nanjing Children's Hospital, Nanjing Medical UniversityNanjing, China; Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University, Quanzhou Normal UniversityQuanzhou, China.
Ming
M
Yang
School of Electronic Science and Engineering, Nanjing University Nanjing, China.
Sidan
S
Du
Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing, China.
Jiquan
J
Yang
Department of Radiology, Zhong-Da Hospital of Southeast University Nanjing, China.
Bin
B
Liu
Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
Juan M
JM
Gorriz
Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
Javier
J
Ramírez
School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; State Key Lab of CAD & CG, Zhejiang UniversityHangzhou, China.
Ti-Fei
TF
Yuan
School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; Key Laboratory of Statistical Information Technology and Data Mining, State Statistics BureauChengdu, China.
Yudong
Y
Zhang
computer aided diagnosis; confusion matrix; directed acyclic graph; sensorineural hearing loss; support vector machine; unilateral hearing loss; wavelet entropy