Anterior Cruciate Ligament Tear Detection Based on Combination of Convolutional Neural Network Enhanced by Improved Human Evolutionary Algorithm
Anterior Cruciate Ligament (ACL) tears are prevalent injuries in sports and physical activities that necessitate prompt and precise diagnosis for optimal treatment and re-habilitation. Conventional diagnostic techniques like physical examination and MRI, may be subjective and protracted. This study...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10988839/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850175131885764608 |
|---|---|
| author | Haibo Shen |
| author_facet | Haibo Shen |
| author_sort | Haibo Shen |
| collection | DOAJ |
| description | Anterior Cruciate Ligament (ACL) tears are prevalent injuries in sports and physical activities that necessitate prompt and precise diagnosis for optimal treatment and re-habilitation. Conventional diagnostic techniques like physical examination and MRI, may be subjective and protracted. This study proposes a new efficient technique for detecting tears of ACL based on the integration of a Convolutional Neural Network (CNN) and an improved version of Human Evolutionary Algorithm (IHEA). The purpose of the suggested IHEA is to enhance the hyperparameters of the CNN to improve its performance in detecting ACL rupture from MRI scans. The suggested technique has been validated by assessing it on a standard case study and comparing its results with some other advanced methods, including the Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), Generative Adversarial Network (GAN2), Gated Recurrent Unit combined with Flexible Fitness Dependent Optimizer (GRU/FFDO), and GRU optimized by Hybrid Tasmanian Devil Optimization (GRU/HTDO). Final results showed the superiority of the proposed model in diagnosing of the ACL tear. |
| format | Article |
| id | doaj-art-2f870438a5534a6abbe39b45bcef4bb6 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2f870438a5534a6abbe39b45bcef4bb62025-08-20T02:19:31ZengIEEEIEEE Access2169-35362025-01-0113884458845710.1109/ACCESS.2025.356730310988839Anterior Cruciate Ligament Tear Detection Based on Combination of Convolutional Neural Network Enhanced by Improved Human Evolutionary AlgorithmHaibo Shen0https://orcid.org/0009-0004-2775-589XNantong Stomatological Hospital, Nantong, ChinaAnterior Cruciate Ligament (ACL) tears are prevalent injuries in sports and physical activities that necessitate prompt and precise diagnosis for optimal treatment and re-habilitation. Conventional diagnostic techniques like physical examination and MRI, may be subjective and protracted. This study proposes a new efficient technique for detecting tears of ACL based on the integration of a Convolutional Neural Network (CNN) and an improved version of Human Evolutionary Algorithm (IHEA). The purpose of the suggested IHEA is to enhance the hyperparameters of the CNN to improve its performance in detecting ACL rupture from MRI scans. The suggested technique has been validated by assessing it on a standard case study and comparing its results with some other advanced methods, including the Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), Generative Adversarial Network (GAN2), Gated Recurrent Unit combined with Flexible Fitness Dependent Optimizer (GRU/FFDO), and GRU optimized by Hybrid Tasmanian Devil Optimization (GRU/HTDO). Final results showed the superiority of the proposed model in diagnosing of the ACL tear.https://ieeexplore.ieee.org/document/10988839/Kneeanterior cruciate ligamenthealthcarediagnosisconvolutional neural networkimproved human evolutionary algorithm |
| spellingShingle | Haibo Shen Anterior Cruciate Ligament Tear Detection Based on Combination of Convolutional Neural Network Enhanced by Improved Human Evolutionary Algorithm IEEE Access Knee anterior cruciate ligament healthcare diagnosis convolutional neural network improved human evolutionary algorithm |
| title | Anterior Cruciate Ligament Tear Detection Based on Combination of Convolutional Neural Network Enhanced by Improved Human Evolutionary Algorithm |
| title_full | Anterior Cruciate Ligament Tear Detection Based on Combination of Convolutional Neural Network Enhanced by Improved Human Evolutionary Algorithm |
| title_fullStr | Anterior Cruciate Ligament Tear Detection Based on Combination of Convolutional Neural Network Enhanced by Improved Human Evolutionary Algorithm |
| title_full_unstemmed | Anterior Cruciate Ligament Tear Detection Based on Combination of Convolutional Neural Network Enhanced by Improved Human Evolutionary Algorithm |
| title_short | Anterior Cruciate Ligament Tear Detection Based on Combination of Convolutional Neural Network Enhanced by Improved Human Evolutionary Algorithm |
| title_sort | anterior cruciate ligament tear detection based on combination of convolutional neural network enhanced by improved human evolutionary algorithm |
| topic | Knee anterior cruciate ligament healthcare diagnosis convolutional neural network improved human evolutionary algorithm |
| url | https://ieeexplore.ieee.org/document/10988839/ |
| work_keys_str_mv | AT haiboshen anteriorcruciateligamentteardetectionbasedoncombinationofconvolutionalneuralnetworkenhancedbyimprovedhumanevolutionaryalgorithm |