Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913818169311232 |
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| author | Al-Nabulsi, Jamal Ahmad, Mohammad Al-Sayed Hasaneiah, Baraa AlZoubi, Fayhaa |
| author_facet | Al-Nabulsi, Jamal Ahmad, Mohammad Al-Sayed Hasaneiah, Baraa AlZoubi, Fayhaa |
| contents | Diagnosing knee osteoarthritis (OA) early is crucial for managing symptoms and preventing further joint damage, ultimately improving patient outcomes and quality of life. In this paper, a bioimpedance-based diagnostic tool that combines precise hardware and deep learning for effective non-invasive diagnosis is proposed. system features a relay-based circuit and strategically placed electrodes to capture comprehensive bioimpedance data. The data is processed by a neural network model, which has been optimized using convolutional layers, dropout regularization, and the Adam optimizer. This approach achieves a 98% test accuracy, making it a promising tool for detecting knee osteoarthritis musculoskeletal disorders. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21512 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning Al-Nabulsi, Jamal Ahmad, Mohammad Al-Sayed Hasaneiah, Baraa AlZoubi, Fayhaa Signal Processing Human-Computer Interaction Machine Learning Diagnosing knee osteoarthritis (OA) early is crucial for managing symptoms and preventing further joint damage, ultimately improving patient outcomes and quality of life. In this paper, a bioimpedance-based diagnostic tool that combines precise hardware and deep learning for effective non-invasive diagnosis is proposed. system features a relay-based circuit and strategically placed electrodes to capture comprehensive bioimpedance data. The data is processed by a neural network model, which has been optimized using convolutional layers, dropout regularization, and the Adam optimizer. This approach achieves a 98% test accuracy, making it a promising tool for detecting knee osteoarthritis musculoskeletal disorders. |
| title | Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning |
| topic | Signal Processing Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2410.21512 |