Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray Images
Osteoarthritis is a deteriorating joint disease affecting millions worldwide. Osteoarthritis is a chronic condition that develops over time due to joint wear and tears. The degeneration of joint cartilage is the underlying cause of osteoarthritis, resulting in bone-to-bone contact and contributing t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10179858/ |
| _version_ | 1797773338588741632 |
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| author | Amjad Rehman Ali Raza Faten S. Alamri Bayan Alghofaily Tanzila Saba |
| author_facet | Amjad Rehman Ali Raza Faten S. Alamri Bayan Alghofaily Tanzila Saba |
| author_sort | Amjad Rehman |
| collection | DOAJ |
| description | Osteoarthritis is a deteriorating joint disease affecting millions worldwide. Osteoarthritis is a chronic condition that develops over time due to joint wear and tears. The degeneration of joint cartilage is the underlying cause of osteoarthritis, resulting in bone-to-bone contact and contributing to stiffness, discomfort, and restricted movement. People with osteoarthritis struggle to perform simple tasks such as walking, standing, or climbing stairs. Moreover, osteoarthritis can also cause psychological distress, including depression and anxiety, due to the chronic pain and disability associated with the condition. Improving the quality of life requires the development of efficient methods for early detection. Our study aims to create a model that can effectively diagnose osteoarthritis in knee X-ray images at an early stage. The advanced deep learning-based Convolutional Neural Network (CNN) and several machine learning-based techniques are applied in comparison. A novel transfer learning-based feature engineering technique CRK (CNN Random forest K-neighbors) is proposed to detect osteoarthritis with high performance. Using a 2D-CNN, the proposed CRK smartly extracts the spatial features from the X-ray images. The spatial features are input to the random forest and k-neighbors techniques, creating a probabilistic feature set. The probabilistic feature set is utilized to build the applied machine learning-based techniques. Extensive study experiments demonstrate that the proposed model outperformed with a 99% accuracy score for predicting osteoarthritis. The performance of each applied model is validated using hyperparameter optimization and k-fold-based cross-validation. The proposed study has the potential to revolutionize the prediction of osteoarthritis from X-ray images with high-performance scores. |
| first_indexed | 2024-03-12T22:05:00Z |
| format | Article |
| id | doaj.art-580242b030734b7da61853a1264daa92 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T22:05:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-580242b030734b7da61853a1264daa922023-07-24T23:00:16ZengIEEEIEEE Access2169-35362023-01-0111713267133810.1109/ACCESS.2023.329454210179858Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray ImagesAmjad Rehman0https://orcid.org/0000-0002-3817-2655Ali Raza1https://orcid.org/0000-0001-5429-9835Faten S. Alamri2https://orcid.org/0000-0003-0312-8731Bayan Alghofaily3Tanzila Saba4https://orcid.org/0000-0003-3138-3801Artificial Intelligence Data Analytics Laboratory (AIDA), CCIS, Prince Sultan University, Riyadh, Saudi ArabiaInstitute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaArtificial Intelligence Data Analytics Laboratory (AIDA), CCIS, Prince Sultan University, Riyadh, Saudi ArabiaArtificial Intelligence Data Analytics Laboratory (AIDA), CCIS, Prince Sultan University, Riyadh, Saudi ArabiaOsteoarthritis is a deteriorating joint disease affecting millions worldwide. Osteoarthritis is a chronic condition that develops over time due to joint wear and tears. The degeneration of joint cartilage is the underlying cause of osteoarthritis, resulting in bone-to-bone contact and contributing to stiffness, discomfort, and restricted movement. People with osteoarthritis struggle to perform simple tasks such as walking, standing, or climbing stairs. Moreover, osteoarthritis can also cause psychological distress, including depression and anxiety, due to the chronic pain and disability associated with the condition. Improving the quality of life requires the development of efficient methods for early detection. Our study aims to create a model that can effectively diagnose osteoarthritis in knee X-ray images at an early stage. The advanced deep learning-based Convolutional Neural Network (CNN) and several machine learning-based techniques are applied in comparison. A novel transfer learning-based feature engineering technique CRK (CNN Random forest K-neighbors) is proposed to detect osteoarthritis with high performance. Using a 2D-CNN, the proposed CRK smartly extracts the spatial features from the X-ray images. The spatial features are input to the random forest and k-neighbors techniques, creating a probabilistic feature set. The probabilistic feature set is utilized to build the applied machine learning-based techniques. Extensive study experiments demonstrate that the proposed model outperformed with a 99% accuracy score for predicting osteoarthritis. The performance of each applied model is validated using hyperparameter optimization and k-fold-based cross-validation. The proposed study has the potential to revolutionize the prediction of osteoarthritis from X-ray images with high-performance scores.https://ieeexplore.ieee.org/document/10179858/OsteoarthritisX-ray imagesknee X-raytransfer learningsmart feature engineeringdeep learning |
| spellingShingle | Amjad Rehman Ali Raza Faten S. Alamri Bayan Alghofaily Tanzila Saba Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray Images IEEE Access Osteoarthritis X-ray images knee X-ray transfer learning smart feature engineering deep learning |
| title | Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray Images |
| title_full | Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray Images |
| title_fullStr | Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray Images |
| title_full_unstemmed | Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray Images |
| title_short | Transfer Learning-Based Smart Features Engineering for Osteoarthritis Diagnosis From Knee X-Ray Images |
| title_sort | transfer learning based smart features engineering for osteoarthritis diagnosis from knee x ray images |
| topic | Osteoarthritis X-ray images knee X-ray transfer learning smart feature engineering deep learning |
| url | https://ieeexplore.ieee.org/document/10179858/ |
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