Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

• Published in: Frontiers in computational neuroscience Vol. 10; p. 106
• Main Authors: Wang, Shuihua, Yang, Ming, Du, Sidan, Yang, Jiquan, Liu, Bin, Gorriz, Juan M., Ramírez, Javier, Yuan, Ti-Fei, Zhang, Yudong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 19.10.2016; Frontiers Media S.A
• Subjects: Alzheimer's disease; Artificial intelligence; Bayesian analysis; Biogeography; Brain; Classification; computer-aided diagnosis; confusion matrix; Deafness; Decision trees; Directed acyclic graph; Ears & hearing; Entropy; Fourier transforms; Hearing loss; Laboratories; Machine learning; Magnetic resonance imaging; Methods; Neural networks; Neurodegenerative diseases; Neuroscience; NMR; Nuclear magnetic resonance; Otology; Patients; Principal components analysis; sensorineural hearing loss; Studies; Support vector machine; Unilateral Hearing Loss; Vision; Wavelet transforms; China; confusion matrix; sensorineural hearing loss; wavelet entropy; support vector machine; directed acyclic graph; computer aided diagnosis; unilateral hearing loss

We 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-cl...