Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images
Abstract Design an effective deep learning-driven method to locate and classify the tibio-femoral knee joint space width (JSW) with respect to both anterior-posterior (AP) and lateral views. Compare the results and see how successfully a deep learning approach can locate and classify tibio-femoral k...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04869-6 |
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| author | S. Sheik Abdullah M. Pallikonda Rajasekaran Mohamed Jakir Hossen Wai Kit Wong Poh Kiat Ng |
| author_facet | S. Sheik Abdullah M. Pallikonda Rajasekaran Mohamed Jakir Hossen Wai Kit Wong Poh Kiat Ng |
| author_sort | S. Sheik Abdullah |
| collection | DOAJ |
| description | Abstract Design an effective deep learning-driven method to locate and classify the tibio-femoral knee joint space width (JSW) with respect to both anterior-posterior (AP) and lateral views. Compare the results and see how successfully a deep learning approach can locate and classify tibio-femoral knee joint osteoarthritis from both anterior-posterior (AP) and lateral-view knee joint x-ray images. To evaluate the performance of a deep learning approach to classify and compare radiographic tibio-femoral knee joint osteoarthritis from both AP and lateral view knee joint digital X-ray images. We use 4334 data points (knee X-ray images) for this study. This paper introduces a methodology to locate, classify, and compare the outcomes of tibio-femoral knee joint osteoarthritis from both AP and lateral knee joint x-ray images. We have fine-tuned DenseNet 201 with transfer learning to extract the features to detect and classify tibio-femoral knee joint osteoarthritis from both AP view and lateral view knee joint X-ray images. The proposed model is compared with some classifiers. The proposed model locate the tibio femoral knee JSW localization accuracy at 98.12% (lateral view) and 99.32% (AP view). The classification accuracy with respect to the lateral view is 92.42% and the AP view is 98.57%, which indicates the performance of automatic detection and classification of tibio-femoral knee joint osteoarthritis with respect to both views (AP and lateral views).We represent the first automated deep learning approach to classify tibio-femoral osteoarthritis on both the AP view and the lateral view, respectively. The proposed deep learning approach trained on the femur and tibial bone regions from both AP view and lateral view digital X-ray images. The proposed model performs better at locating and classifying tibio femoral knee joint osteoarthritis than the existing approaches. The proposed approach will be helpful for the clinicians/medical experts to analyze the progression of tibio-femoral knee OA in different views. The proposed approach performs better in AP view than Lateral view. So, when compared to other continuing existing architectures/models, the proposed model offers exceptional outcomes with fine-tuning. |
| format | Article |
| id | doaj-art-6c7a9aa401e042e49e5f2b2ffe422b3f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6c7a9aa401e042e49e5f2b2ffe422b3f2025-08-20T04:01:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-04869-6Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray imagesS. Sheik Abdullah0M. Pallikonda Rajasekaran1Mohamed Jakir Hossen2Wai Kit Wong3Poh Kiat Ng4School of Electrical and Electronics Technology, Kalasalingam Academy of Research and EducationSchool of Electrical and Electronics Technology, Kalasalingam Academy of Research and EducationFaculty of Engineering and Technology, Multimedia UniversityFaculty of Engineering and Technology, Multimedia UniversityFaculty of Engineering and Technology, Multimedia UniversityAbstract Design an effective deep learning-driven method to locate and classify the tibio-femoral knee joint space width (JSW) with respect to both anterior-posterior (AP) and lateral views. Compare the results and see how successfully a deep learning approach can locate and classify tibio-femoral knee joint osteoarthritis from both anterior-posterior (AP) and lateral-view knee joint x-ray images. To evaluate the performance of a deep learning approach to classify and compare radiographic tibio-femoral knee joint osteoarthritis from both AP and lateral view knee joint digital X-ray images. We use 4334 data points (knee X-ray images) for this study. This paper introduces a methodology to locate, classify, and compare the outcomes of tibio-femoral knee joint osteoarthritis from both AP and lateral knee joint x-ray images. We have fine-tuned DenseNet 201 with transfer learning to extract the features to detect and classify tibio-femoral knee joint osteoarthritis from both AP view and lateral view knee joint X-ray images. The proposed model is compared with some classifiers. The proposed model locate the tibio femoral knee JSW localization accuracy at 98.12% (lateral view) and 99.32% (AP view). The classification accuracy with respect to the lateral view is 92.42% and the AP view is 98.57%, which indicates the performance of automatic detection and classification of tibio-femoral knee joint osteoarthritis with respect to both views (AP and lateral views).We represent the first automated deep learning approach to classify tibio-femoral osteoarthritis on both the AP view and the lateral view, respectively. The proposed deep learning approach trained on the femur and tibial bone regions from both AP view and lateral view digital X-ray images. The proposed model performs better at locating and classifying tibio femoral knee joint osteoarthritis than the existing approaches. The proposed approach will be helpful for the clinicians/medical experts to analyze the progression of tibio-femoral knee OA in different views. The proposed approach performs better in AP view than Lateral view. So, when compared to other continuing existing architectures/models, the proposed model offers exceptional outcomes with fine-tuning.https://doi.org/10.1038/s41598-025-04869-6Tibio-femoral osteoarthritisAP viewLateral viewDigital X-ray imagesDenseNet 201 |
| spellingShingle | S. Sheik Abdullah M. Pallikonda Rajasekaran Mohamed Jakir Hossen Wai Kit Wong Poh Kiat Ng Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images Scientific Reports Tibio-femoral osteoarthritis AP view Lateral view Digital X-ray images DenseNet 201 |
| title | Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images |
| title_full | Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images |
| title_fullStr | Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images |
| title_full_unstemmed | Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images |
| title_short | Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images |
| title_sort | deep learning based classification of tibio femoral knee osteoarthritis from lateral view knee joint x ray images |
| topic | Tibio-femoral osteoarthritis AP view Lateral view Digital X-ray images DenseNet 201 |
| url | https://doi.org/10.1038/s41598-025-04869-6 |
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