Automated detection of knee cartilage region in X-ray image
The prevalence of a symptomatic knee or osteoarthritis (OA) is approximately 9.6% in men and 18.0% in women over 60 years of age according to the OARSI 2016 report. Using early on-stage clinical qualitative assessments through means of X-ray scans, the cartilage health and degradation of an individu...
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37245/ http://umpir.ump.edu.my/id/eprint/37245/1/Automated%20detection%20of%20knee%20cartilage%20region%20in%20Xray%20image.pdf |
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| author | Teo, Jia Chern Mohd Khairuddin, Ismail Mohd Razman, Mohd Azraai Anwar, P. P. Abdul Majeed Mohd Isa, Wan Hasbullah |
| author_facet | Teo, Jia Chern Mohd Khairuddin, Ismail Mohd Razman, Mohd Azraai Anwar, P. P. Abdul Majeed Mohd Isa, Wan Hasbullah |
| author_sort | Teo, Jia Chern |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The prevalence of a symptomatic knee or osteoarthritis (OA) is approximately 9.6% in men and 18.0% in women over 60 years of age according to the OARSI 2016 report. Using early on-stage clinical qualitative assessments through means of X-ray scans, the cartilage health and degradation of an individual can be monitored through cartilage shape and surface over time. In this paper, we implement the application of transfer learning models such as InceptionV3, Xception and DenseNet201 for feature extraction of a rebalanced 1,000 knee X-ray images taken from Osteoarthritis Initiative (OAI) dataset with 5 classes graded 0–4 according to Kellgren-Lawrence grading split into a 70/15/15 training/validation/testing split. The features extracted are subsequently fed into machine learning classifiers, namely support vector machine (SVM). An average multiclass accuracy of 71.33% was achieved for hyperparameter fine-tuned DenseNet201-SVM model. |
| first_indexed | 2025-11-15T03:25:08Z |
| format | Article |
| id | ump-37245 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:25:08Z |
| publishDate | 2022 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-372452023-03-09T03:56:57Z http://umpir.ump.edu.my/id/eprint/37245/ Automated detection of knee cartilage region in X-ray image Teo, Jia Chern Mohd Khairuddin, Ismail Mohd Razman, Mohd Azraai Anwar, P. P. Abdul Majeed Mohd Isa, Wan Hasbullah T Technology (General) TK Electrical engineering. Electronics Nuclear engineering TS Manufactures The prevalence of a symptomatic knee or osteoarthritis (OA) is approximately 9.6% in men and 18.0% in women over 60 years of age according to the OARSI 2016 report. Using early on-stage clinical qualitative assessments through means of X-ray scans, the cartilage health and degradation of an individual can be monitored through cartilage shape and surface over time. In this paper, we implement the application of transfer learning models such as InceptionV3, Xception and DenseNet201 for feature extraction of a rebalanced 1,000 knee X-ray images taken from Osteoarthritis Initiative (OAI) dataset with 5 classes graded 0–4 according to Kellgren-Lawrence grading split into a 70/15/15 training/validation/testing split. The features extracted are subsequently fed into machine learning classifiers, namely support vector machine (SVM). An average multiclass accuracy of 71.33% was achieved for hyperparameter fine-tuned DenseNet201-SVM model. Penerbit UMP 2022-06 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/37245/1/Automated%20detection%20of%20knee%20cartilage%20region%20in%20Xray%20image.pdf Teo, Jia Chern and Mohd Khairuddin, Ismail and Mohd Razman, Mohd Azraai and Anwar, P. P. Abdul Majeed and Mohd Isa, Wan Hasbullah (2022) Automated detection of knee cartilage region in X-ray image. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 4 (1). pp. 104-109. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v4i1.8627 https://doi.org/10.15282/mekatronika.v4i1.8627 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Teo, Jia Chern Mohd Khairuddin, Ismail Mohd Razman, Mohd Azraai Anwar, P. P. Abdul Majeed Mohd Isa, Wan Hasbullah Automated detection of knee cartilage region in X-ray image |
| title | Automated detection of knee cartilage region in X-ray image |
| title_full | Automated detection of knee cartilage region in X-ray image |
| title_fullStr | Automated detection of knee cartilage region in X-ray image |
| title_full_unstemmed | Automated detection of knee cartilage region in X-ray image |
| title_short | Automated detection of knee cartilage region in X-ray image |
| title_sort | automated detection of knee cartilage region in x-ray image |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/37245/ http://umpir.ump.edu.my/id/eprint/37245/ http://umpir.ump.edu.my/id/eprint/37245/ http://umpir.ump.edu.my/id/eprint/37245/1/Automated%20detection%20of%20knee%20cartilage%20region%20in%20Xray%20image.pdf |