OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging
BackgroundKnee osteoarthritis (KOA) constitutes the prevailing manifestation of arthritis. Radiographs function as a common modality for primary screening; however, traditional X-ray evaluation of osteoarthritis confronts challenges such as reduced sensitivity, subjective interpretation, and heighte...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2024.1437188/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841561046682173440 |
|---|---|
| author | Xiaolu Ren Xiaolu Ren Lingxuan Hou Shan Liu Peng Wu Siming Liang Haitian Fu Chengquan Li Ting Li Yongjing Cheng |
| author_facet | Xiaolu Ren Xiaolu Ren Lingxuan Hou Shan Liu Peng Wu Siming Liang Haitian Fu Chengquan Li Ting Li Yongjing Cheng |
| author_sort | Xiaolu Ren |
| collection | DOAJ |
| description | BackgroundKnee osteoarthritis (KOA) constitutes the prevailing manifestation of arthritis. Radiographs function as a common modality for primary screening; however, traditional X-ray evaluation of osteoarthritis confronts challenges such as reduced sensitivity, subjective interpretation, and heightened misdiagnosis rates. The objective of this investigation is to enhance the validation and optimization of accuracy and efficiency in KOA assessment by utilizing fusion deep learning techniques.MethodsThis study aims to develop a highly accurate and lightweight model for automatically predicting and classifying KOA through knee X-ray imaging. We propose a deep learning model named OA-MEN, which integrates a hybrid model combining ResNet and MobileNet feature extraction with multi-scale feature fusion. This approach ensures enhanced extraction of semantic information without losing the advantages of large feature maps provided by high image resolution in lower layers of the network. This effectively expands the model’s receptive field and strengthens its understanding capability. Additionally, we conducted unseen-data tests and compared our model with widely used baseline models to highlight its superiority over conventional approaches.ResultsThe OA-MEN model demonstrated exceptional performance in tests. In the unseen-data test, our model achieved an average accuracy (ACC) of 84.88% and an Area Under the Curve (AUC) of 89.11%, marking improvements over the best-performing baseline models. These results showcase its improved capability in predicting KOA from X-ray images, making it a promising tool for assisting radiologists in diagnosis and treatment selection in clinical settings.ConclusionLeveraging deep learning for osteoarthritis classification guarantees heightened efficiency and accuracy. The future goal is to seamlessly integrate deep learning and advanced computational techniques with the expertise of medical professionals. |
| format | Article |
| id | doaj-art-90199d4ca5654ef0b60ca3a1b001931f |
| institution | Kabale University |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-90199d4ca5654ef0b60ca3a1b001931f2025-01-03T06:47:16ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-01-011210.3389/fbioe.2024.14371881437188OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imagingXiaolu Ren0Xiaolu Ren1Lingxuan Hou2Shan Liu3Peng Wu4Siming Liang5Haitian Fu6Chengquan Li7Ting Li8Yongjing Cheng9Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, ChinaSchool of Health Sciences, Universiti Sains Malaysia, Kota Bharu, Kelantan, MalaysiaCollege of Biomedical Engineering, Sichuan University, Chengdu, Sichuan, ChinaDepartment of Radiology, General Hospital of Ningxia Medical University, Yinchuan, ChinaDepartment of Orthopedics, General Hospital of Ningxia Medical University, Yinchuan, ChinaDepartment of Orthopedics, General Hospital of Ningxia Medical University, Yinchuan, ChinaSchool of Clinical Medicine, Tsinghua University, Beijing, ChinaSchool of Clinical Medicine, Tsinghua University, Beijing, ChinaDepartment of Radiology, General Hospital of Ningxia Medical University, Yinchuan, ChinaDepartment of Rheumatology and Immunology, Beijing Hospital, National Centre of Gerontology, Beijing, ChinaBackgroundKnee osteoarthritis (KOA) constitutes the prevailing manifestation of arthritis. Radiographs function as a common modality for primary screening; however, traditional X-ray evaluation of osteoarthritis confronts challenges such as reduced sensitivity, subjective interpretation, and heightened misdiagnosis rates. The objective of this investigation is to enhance the validation and optimization of accuracy and efficiency in KOA assessment by utilizing fusion deep learning techniques.MethodsThis study aims to develop a highly accurate and lightweight model for automatically predicting and classifying KOA through knee X-ray imaging. We propose a deep learning model named OA-MEN, which integrates a hybrid model combining ResNet and MobileNet feature extraction with multi-scale feature fusion. This approach ensures enhanced extraction of semantic information without losing the advantages of large feature maps provided by high image resolution in lower layers of the network. This effectively expands the model’s receptive field and strengthens its understanding capability. Additionally, we conducted unseen-data tests and compared our model with widely used baseline models to highlight its superiority over conventional approaches.ResultsThe OA-MEN model demonstrated exceptional performance in tests. In the unseen-data test, our model achieved an average accuracy (ACC) of 84.88% and an Area Under the Curve (AUC) of 89.11%, marking improvements over the best-performing baseline models. These results showcase its improved capability in predicting KOA from X-ray images, making it a promising tool for assisting radiologists in diagnosis and treatment selection in clinical settings.ConclusionLeveraging deep learning for osteoarthritis classification guarantees heightened efficiency and accuracy. The future goal is to seamlessly integrate deep learning and advanced computational techniques with the expertise of medical professionals.https://www.frontiersin.org/articles/10.3389/fbioe.2024.1437188/fullknee osteoarthritisdeep learningdecision makingartificial intelligenceconvolution natural network |
| spellingShingle | Xiaolu Ren Xiaolu Ren Lingxuan Hou Shan Liu Peng Wu Siming Liang Haitian Fu Chengquan Li Ting Li Yongjing Cheng OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging Frontiers in Bioengineering and Biotechnology knee osteoarthritis deep learning decision making artificial intelligence convolution natural network |
| title | OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging |
| title_full | OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging |
| title_fullStr | OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging |
| title_full_unstemmed | OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging |
| title_short | OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging |
| title_sort | oa men a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using x ray imaging |
| topic | knee osteoarthritis deep learning decision making artificial intelligence convolution natural network |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2024.1437188/full |
| work_keys_str_mv | AT xiaoluren oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT xiaoluren oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT lingxuanhou oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT shanliu oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT pengwu oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT simingliang oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT haitianfu oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT chengquanli oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT tingli oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging AT yongjingcheng oamenafusiondeeplearningapproachforenhancedaccuracyinkneeosteoarthritisdetectionandclassificationusingxrayimaging |