Hearing loss classification via stationary wavelet entropy and genetic algorithm

Abstract

The accompanying symptoms of hearing loss is slow and sensory, which makes detecting hearing loss of huge significance to the medical diagnosis and scientific research field. To improve the efficiency of hearing loss classification, we conducted a research on a dataset obtained from magnetic resonance imaging and presented a novel computer aided system based on stationary wavelet entropy, k-fold cross validation, single-hidden-layer feedforward neural network and genetic algorithm. Firstly, the features are extracted from each hearing loss image via stationary wavelet entropy. Then, we used the genetic algorithm to train the single-hidden-layer feedforward neural network. The system reaches an overall sensitivity of 89.89±2.50%, which means the model gives much better performance than manual interpretation.