Hearing loss detection by discrete wavelet transform and multi-layer perceptron trained by nature-inspired algorithms

(Aim) For detecting the hearing loss (HL) more accurately and efficiently, the new computer-aid diagnosis (CAD) based on a nature-inspired algorithm (NIAs) is proposed in this study. (Method) First, the discrete wavelet transform (DWT) is used for extracting texture features from the brain images, and then the principle component analysis (PCA) is employed to decrease the dimension of features. Second, the Multi-Layer Perceptron (MLP) is used as a classifier. Traditional gradient-based descent algorithms are vulnerable to get struck at local minima; thus, the NIAs are introduced. The differential evolution algorithm (DE), particle swarm optimization (PSO), artificial bee colony algorithm (ABC), and improved ABC (IABC) are employed to train MLP. Because the ordinary ABC is good at exploration but gives a poor performance at exploitation, therefore a new model of ABC, called IABC is proposed. The K-fold validation method is utilized to measure the performance of the CAD. (Result) To verify the performance of our method, The CAD based on IABC is compared with state-of-the-art-approaches. (Conclusion) The experiment results show that the overall accuracy of our method has the highest overall accuracy among five approaches. Therefore, the proposed CAD is effective method for detecting hearing loss.