APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection
Abstract 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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2023-07-01
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| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12114 |
| _version_ | 1797420602507657216 |
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| author | Jingyuan Yang Yu‐Dong Zhang |
| author_facet | Jingyuan Yang Yu‐Dong Zhang |
| author_sort | Jingyuan Yang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-09T07:04:48Z |
| format | Article |
| id | doaj.art-1400fb8f5ed24049b4ce2849686bc548 |
| institution | Directory Open Access Journal |
| issn | 2047-4938 2047-4946 |
| language | English |
| last_indexed | 2024-03-09T07:04:48Z |
| publishDate | 2023-07-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj.art-1400fb8f5ed24049b4ce2849686bc5482023-12-03T09:43:28ZengHindawi-IETIET Biometrics2047-49382047-49462023-07-0112421122110.1049/bme2.12114APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detectionJingyuan Yang0Yu‐Dong Zhang1School of Computing and Mathematical Sciences University of Leicester Leicester UKSchool of Computing and Mathematical Sciences University of Leicester Leicester UKAbstract 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.https://doi.org/10.1049/bme2.12114medical image processingpattern classification |
| spellingShingle | Jingyuan Yang Yu‐Dong Zhang APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection IET Biometrics medical image processing pattern classification |
| title | APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection |
| title_full | APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection |
| title_fullStr | APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection |
| title_full_unstemmed | APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection |
| title_short | APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection |
| title_sort | appso nn an adaptive probability particle swarm optimization neural network for sensorineural hearing loss detection |
| topic | medical image processing pattern classification |
| url | https://doi.org/10.1049/bme2.12114 |
| work_keys_str_mv | AT jingyuanyang appsonnanadaptiveprobabilityparticleswarmoptimizationneuralnetworkforsensorineuralhearinglossdetection AT yudongzhang appsonnanadaptiveprobabilityparticleswarmoptimizationneuralnetworkforsensorineuralhearinglossdetection |