APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection

• Published in: IET biometrics Vol. 12; no. 4; pp. 211 - 221
• Main Authors: Yang, Jingyuan, Zhang, Yu‐Dong
• Format: Journal Article
• Language: English
• Published: Stevenage John Wiley & Sons, Inc 01.07.2023; Wiley
• Subjects: Algorithms; Artificial neural networks; Computer simulation; Congenital diseases; Decomposition; Disease; Ears & hearing; Eigenvalues; Entropy; Evolutionary algorithms; Fourier transforms; Gender; Genetic algorithms; Hearing; Hearing loss; Magnetic resonance imaging; medical image processing; Neural networks; Optimization algorithms; Otology; Particle swarm optimization; pattern classification; Search algorithms; Support vector machines; Wavelet transforms
• ISSN: 2047-4938; 2047-4946; 2047-4946
• DOI: 10.1049/bme2.12114

As a hearing disorder, sensorineural hearing loss (SNHL) can be effectively detected by magnetic resonance imaging (MRI). However, the manual detection of MRI scanning is subjective, time‐consuming, and unpredictable. An accurate and automatic computer‐aided diagnosis system is proposed for SNHL detection, providing reliable references for professionals. The system first employs a wavelet entropy layer to extract features of MRI images. Then, a neural network layer is proposed as the classifier consisting of a feedforward neural network (FNN) and an adaptive‐probability PSO (APPSO) algorithm. The authors prove the rotation‐variant property of the basic particle swarm optimization (PSO) by the algebraic property of matrix transformation. The property is unsuitable for optimising parameters of neural networks. Thus, in APPSO, the authors integrate the new update rules based on all‐dimensional variation and adaptive‐probability mechanism into the basic PSO, which can improve its searching ability without losing population diversity. The authors compare APPSO‐NN with FNN trained by five popular evolutionary algorithms. The simulation results show that APPSO performs best in training FNN. The method also compares with six state‐of‐the‐art methods. The simulation results show that the best performance in sensitivity and overall accuracy of hearing loss classification, which proves that the method is effective and promising for SNHL detection. The authors first propose a wavelet entropy layer to extract features of MRI images. They then propose a neural network layer as the classifier consisting of a feedforward neural network and adaptive‐probability PSO.