Abstract
Introduction: Sensorineural hearing loss is associated with many complications and needs timely detection and diagnosis. Objectives: Optimize the sensorineural hearing loss detection system to improve the accuracies of image detection. Method: The stationary wavelet entropy was used to extra
Highlights
Sensorineural hearing loss is associated with many complications and needs timely detection and diagnosis
The stationary wavelet entropy was used to extract the features of NMR images, the single hidden layer neural network was used for classification, and the Biogeography-based Optimization (BBO) algorithm was used for optimization to avoid the dilemma of local optimum
MRI can show the lesions of soft tissue and intracranial structure well, especially the application of MRI water imaging technology makes it possible to display the fine structure of the inner eardrum labyrinth
Summary
Sensorineural hearing loss is associated with many complications and needs timely detection and diagnosis. Sensorineural hearing loss (SNHL) is the most common sensory deficit in the world, which caused by the dysfunction of one or more parts of the auditory pathway between the inner ear and the auditory cortex [1]. F. Liu(2017) et al [10] proposed to combine wavelet entropy with feedforward neural network trained by genetic algorithm to defect hearing loss. Liu(2017) et al [10] proposed to combine wavelet entropy with feedforward neural network trained by genetic algorithm to defect hearing loss Their method using 4-level decomposition yielded the overall accuracy of 81.11±1.34%. Their earlier application of genetic algorithm for hearing loss detection system provides ideas for many subsequent researchers. Fang-zhou BAO (2018) et al [11] defected hearing loss via Wavelet Entropy and Particle Swarm Optimized Trained Support Vector
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