MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection

Sensorineural hearing loss (SNHL) is a common hearing disorder or deafness which accounts for about 90% of the reported hearing loss. Magnetic resonance imaging (MRI) has been found to be an effective neuroimaging technique for detecting SNHL. However, manual detection methods, mainly based on the visual inspection of MRI, are cumbersome, time-consuming and need skilled supervision. Hence, there is a great need to design a computer-aided detection system for fast, accurate and automated detection of SNHL. This paper presents a new method for automated diagnosis of SNHL through brain MR images. Fast discrete curvelet transform is employed for image decomposition. The features are extracted from various decomposed subbands at different scales and orientations. A set of discriminant features is then derived using PCA+LDA algorithm. A hybrid classifier is suggested by integrating extreme learning machine and Jaya optimization with mutation (MJaya-ELM) to distinguish hearing loss images from healthy MR images. The proposed hybrid method overcomes the drawbacks of traditional ELM and other learning algorithms for single layer feedforward neural network. The concept of mutation is introduced to conventional Jaya optimization (MJaya) for improving the global search ability of the solutions by providing additional diversity. The proposed system is evaluated on a well-studied database. The comparison results demonstrate that the proposed scheme outperforms the existing schemes in terms of overall accuracy and sensitivity over different classes. The effectiveness of the proposed MJaya-ELM algorithm is also compared with its counterparts such as PSO-ELM, DE-ELM, and Jaya-ELM, and the results indicate the superiority of MJaya-ELM.