Sensorineural Hearing Loss Identification via Discrete Wavelet Packet Entropy and Cat Swarm Optimization

(Aim) Currently, there are many methods to identify sensorineural hearing loss via magnetic resonance imaging. This study aims to develop a more efficient approach. (Methods) Our approach used discrete wavelet packet entropy as the feature-extraction method. It used single-hidden layer feedforward neural network as the classifier model. A bio-inspired algorithm, cat swarm optimization (CSO) is employed to train the weights/biases of this neural network. (Results) Simulation results showed our approach achieved an overall accuracy of 92.33%. Besides, this cat swarm optimization method gives better performance than genetic algorithm, particle swarm optimization, firefly algorithm, chaotic simulated annealing, and adaptive genetic algorithm. The whole hearing loss identification system yields greater accuracy than three state-of-the-art methods. It costs 102.8 ms to predict a new brain image. (Conclusion) The discrete wavelet packet entropy is an efficient feature-extraction method for detecting hearing loss. The CSO algorithm may be applied in other optimization problems.

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