Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique
Abstract Knee osteoarthritis (KOA) represents a well-documented degenerative arthropathy prevalent among the elderly population. KOA is a persistent condition, also referred to as progressive joint Disease, stemming from the continual deterioration of cartilage. Predominantly afflicting individuals...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78203-x |
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| author | Punita Panwar Sandeep Chaurasia Jayesh Gangrade Ashwani Bilandi Dayananda Pruthviraja |
| author_facet | Punita Panwar Sandeep Chaurasia Jayesh Gangrade Ashwani Bilandi Dayananda Pruthviraja |
| author_sort | Punita Panwar |
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| description | Abstract Knee osteoarthritis (KOA) represents a well-documented degenerative arthropathy prevalent among the elderly population. KOA is a persistent condition, also referred to as progressive joint Disease, stemming from the continual deterioration of cartilage. Predominantly afflicting individuals aged 45 and above, this ailment is commonly labeled as a “wear and tear” joint disorder, targeting joints such as the knee, hand, hips, and spine. Osteoarthritis symptoms typically increase gradually, contributing to the deterioration of articular cartilage. Prominent indicators encompass pain, stiffness, tenderness, swelling, and the development of bone spurs. Diagnosis typically involves the utilization of Radiographic X-ray images, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) Scan by medical professionals and experts. However, this conventional approach is time-consuming, and also sometimes tedious for medical professionals. In order to address the limitation of time and expedite the diagnostic process, deep learning algorithms have been implemented in the medical field. In the present investigation, four pre-trained models, specifically CNN, AlexNet, ResNet34 and ResNet-50, were utilized to predict the severity of KOA. Further, a Deep stack ensemble technique was employed to achieve optimal performance resulting to the accuracy of 99.71%. |
| format | Article |
| id | doaj-art-9d890839547b4d40b7d9410041b4128d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-9d890839547b4d40b7d9410041b4128d2024-11-10T12:18:00ZengNature PortfolioScientific Reports2045-23222024-11-0114111410.1038/s41598-024-78203-xOptimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble techniquePunita Panwar0Sandeep Chaurasia1Jayesh Gangrade2Ashwani Bilandi3Dayananda Pruthviraja4Department of Computer Science & Engineering, School of Computer Science & Engineering, Manipal University JaipurDepartment of Computer Science & Engineering, School of Computer Science & Engineering, Manipal University JaipurDepartment of Artificial Intelligence & Machine Learning, Computer Science & Engineering, Manipal University JaipurDepartment of Orthopedics , MBBS, Mahatma Gandhi Medical CollegeDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationAbstract Knee osteoarthritis (KOA) represents a well-documented degenerative arthropathy prevalent among the elderly population. KOA is a persistent condition, also referred to as progressive joint Disease, stemming from the continual deterioration of cartilage. Predominantly afflicting individuals aged 45 and above, this ailment is commonly labeled as a “wear and tear” joint disorder, targeting joints such as the knee, hand, hips, and spine. Osteoarthritis symptoms typically increase gradually, contributing to the deterioration of articular cartilage. Prominent indicators encompass pain, stiffness, tenderness, swelling, and the development of bone spurs. Diagnosis typically involves the utilization of Radiographic X-ray images, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) Scan by medical professionals and experts. However, this conventional approach is time-consuming, and also sometimes tedious for medical professionals. In order to address the limitation of time and expedite the diagnostic process, deep learning algorithms have been implemented in the medical field. In the present investigation, four pre-trained models, specifically CNN, AlexNet, ResNet34 and ResNet-50, were utilized to predict the severity of KOA. Further, a Deep stack ensemble technique was employed to achieve optimal performance resulting to the accuracy of 99.71%.https://doi.org/10.1038/s41598-024-78203-xKnee osteoarthritisMagnetic resonance imaging (MRI)Deep learning algorithmsConvolutional neural networkDeep Stack Ensemble |
| spellingShingle | Punita Panwar Sandeep Chaurasia Jayesh Gangrade Ashwani Bilandi Dayananda Pruthviraja Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique Scientific Reports Knee osteoarthritis Magnetic resonance imaging (MRI) Deep learning algorithms Convolutional neural network Deep Stack Ensemble |
| title | Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique |
| title_full | Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique |
| title_fullStr | Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique |
| title_full_unstemmed | Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique |
| title_short | Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique |
| title_sort | optimizing knee osteoarthritis severity prediction on mri images using deep stacking ensemble technique |
| topic | Knee osteoarthritis Magnetic resonance imaging (MRI) Deep learning algorithms Convolutional neural network Deep Stack Ensemble |
| url | https://doi.org/10.1038/s41598-024-78203-x |
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