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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

Osteoarthritis is a condition that poses a risk to the knee joint, resulting in pain and impaired function. However, traditional knee X-ray evaluations using the Kellgren-Lawrence grading system have proven to be inefficient. These evaluations are subjective, time-consuming, and labor-intensive, particularly in busy hospital settings.

Objective

The objective of this research was to present a deep learning-based approach that can detect knee joint regions in medical images. By addressing the limitations of traditional methods, the aim was to develop a more efficient and automated approach for knee joint analysis.

Methods

The proposed method utilizes the Faster R-CNN model, which consists of a region proposal network (RPN) and Fast R-CNN. The RPN generates region proposals that potentially contain knee joint regions, while the Fast R-CNN network categorizes and extracts features from these proposals. To train the model, a dataset of knee joint images was employed. The performance of the model was evaluated using metrics, such as accuracy, precision, recall, F1-score, and mean IoU (Intersection Over Union).

Results

The results demonstrated the high accuracy of the proposed method in detecting knee joint regions. The model achieved a mean IoU of 94.5, indicating a strong overlap between the predicted and ground truth regions. These findings highlight the potential of deep learning-based approaches in automating medical image analysis, specifically in the diagnosis and management of knee joint disorders.

Conclusion

This study emphasizes the significance of leveraging advanced technologies, such as deep learning, in medical imaging. By developing more efficient and accurate methods for identifying knee joint regions in medical images, it becomes feasible to enhance patient outcomes and healthcare delivery. The proposed deep learning-based approach showcases promising results, paving the way for further advancements in the field of medical image analysis and contributing to improved diagnostic capabilities for knee joint disorders.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2023-10-19
2025-06-16

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References

  1. ChenP. GaoL. ShiX. AllenK. YangL. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss.Comput. Med. Imaging Graph.201975849210.1016/j.compmedimag.2019.06.00231238184
     [Google Scholar]
  2. SainiD. ChandT. ChouhanD.K. PrakashM. A comparative analysis of automatic classification and grading methods for knee osteoarthritis focussing on X-ray images.Biocybern. Biomed. Eng.202141241944410.1016/j.bbe.2021.03.002
     [Google Scholar]
  3. KohnM.D. SassoonA.A. FernandoN.D. Classifications in brief: Kellgren-Lawrence classification of osteoarthritis.Clin. Orthop. Relat. Res.201647481886189310.1007/s11999‑016‑4732‑426872913
     [Google Scholar]
  4. PoudelS. A study of disease diagnosis using machine learning.2nd International Electronic Conference on Healthcare17 February–3 March202210.3390/IECH2022‑12311
     [Google Scholar]
  5. GoceriE. SongulC. Biomedical information technology: Image based computer aided diagnosis systems.International Conference on Advanced TechnologiesAntalaya, Turkey2018
     [Google Scholar]
  6. GoceriN. GoceriE. A neural network based kidney segmentation from MR images.2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)09-11 December 2015Miami, FL, USA201510.1109/ICMLA.2015.229
     [Google Scholar]
  7. Göçeri̇E. ÜnlüM.Z. Di̇cleO. A comparative performance evaluation of various approaches for liver segmentation from SPIR images.Turk. J. Electr. Eng. Comput. Sci.201523374176810.3906/elk‑1304‑36
     [Google Scholar]
  8. GoceriE. MartinezE. Artificial neural network based abdominal organ segmentations: A review.2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)09-11 December 2015Miami, FL, USA201510.1109/ICMLA.2015.231
     [Google Scholar]
  9. GöçeriE. Impact of deep learning and smartphone technologies in dermatology: Automated diagnosis.2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)09-12 November 2020Paris, France202010.1109/IPTA50016.2020.9286706
     [Google Scholar]
  10. SabaL. BiswasM. KuppiliV. Cuadrado GodiaE. SuriH.S. EdlaD.R. OmerzuT. LairdJ.R. KhannaN.N. MavrogeniS. ProtogerouA. SfikakisP.P. ViswanathanV. KitasG.D. NicolaidesA. GuptaA. SuriJ.S. The present and future of deep learning in radiology.Eur. J. Radiol.2019114142410.1016/j.ejrad.2019.02.03831005165
     [Google Scholar]
  11. EvginGÖÇERİ An application for automated diagnosis of facial dermatological diseases.İzmir Katip Çelebi Univ. Sağlık Bilim. Derg.2021639199
     [Google Scholar]
  12. McBeeM.P. AwanO.A. ColucciA.T. GhobadiC.W. KadomN. KansagraA.P. TridandapaniS. AuffermannW.F. Deep learning in radiology.Acad. Radiol.201825111472148010.1016/j.acra.2018.02.01829606338
     [Google Scholar]
  13. GöçeriE. Convolutional neural network based desktop applications to classify dermatological diseases.2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS)09-11 December 2020Genova, Italy202010.1109/IPAS50080.2020.9334956
     [Google Scholar]
  14. GoceriE. Challenges and recent solutions for image segmentation in the era of deep learning.2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)06-09 November 2019Istanbul, Turkey201910.1109/IPTA.2019.8936087
     [Google Scholar]
  15. GoceriE. Medical image data augmentation: techniques, comparisons and interpretations.Artif. Intell. Rev.2023•••14510.1007/s10462‑023‑10453‑z37362888
     [Google Scholar]
  16. GoceriE. Comparison of the impacts of dermoscopy image augmentation methods on skin cancer classification and a new augmentation method with wavelet packets.Int. J. Imaging Syst. Technol.2023ima.2289010.1002/ima.22890
     [Google Scholar]
  17. GoceriE. Image augmentation for deep learning based lesion classification from skin images.2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS)09-11 December 2020Genova, Italy202010.1109/IPAS50080.2020.9334937
     [Google Scholar]
  18. AlmutairiS.M. ManimuruganS. MajedM.A. NarmathaC. SubramaniamG. KarthikeyanP. An efficient USE-net deep learning model for cancer detection.Int. J. Intell. Syst.20232023
     [Google Scholar]
  19. El-GhanyS.A. ElmogyM. El-AzizA.A.A. A fully automatic fine tuned deep learning model for knee osteoarthritis detection and progression analysis.Egyptian Informatics Journal202324222924010.1016/j.eij.2023.03.005
     [Google Scholar]
  20. LimJ. KimJ. CheonS. A deep neural network-based method for early detection of osteoarthritis using statistical data.Int. J. Environ. Res. Public Health2019167128110.3390/ijerph1607128130974803
     [Google Scholar]
  21. KellgrenJ.H. LawrenceJ.S. Radiological assessment of osteo-arthrosis.Ann. Rheum. Dis.195716449450210.1136/ard.16.4.49413498604
     [Google Scholar]
  22. AltmanR.D. GoldG.E. Atlas of individual radiographic features in osteoarthritis, revised.Osteoarthritis Cartilage200715Suppl. AA1A5610.1016/j.joca.2006.11.00917320422
     [Google Scholar]
  23. ShamirL. LingS.M. ScottW.W.Jr BosA. OrlovN. MacuraT.J. EckleyD.M. FerrucciL. GoldbergI.G. Knee x-ray image analysis method for automated detection of osteoarthritis.IEEE Trans. Biomed. Eng.200956240741510.1109/TBME.2008.200602519342330
     [Google Scholar]
  24. AntonyJ. McGuinnessK. O’ConnorN.E. MoranK. Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks.2016 23rd International Conference on Pattern Recognition (ICPR)20th Scandinavian Conference, SCIA 2017119512002016
     [Google Scholar]
  25. TiulpinA. JeromeT. EsaR. SimoS. A novel method for automatic localization of joint area on knee plain radiographs.Image Analysis: 20th Scandinavian Conference, SCIA 20172017Tromsø, NorwayJune 12–14, 2017290301
     [Google Scholar]
  26. AntonyJ. McGuinnessK. MoranK. O’ConnorN.E. Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks.13th International Conference, MLDM 2017July 15-20, 2017New York, NY, USA376390201710.1007/978‑3‑319‑62416‑7_27
     [Google Scholar]
  27. SaleemM. FaridM.S. SaleemS. KhanM.H. X-ray image analysis for automated knee osteoarthritis detection.Signal Image Video Process.20201461079108710.1007/s11760‑020‑01645‑z
     [Google Scholar]
  28. LiuB. LuoJ. HuangH. Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN.Int. J. CARS202015345746610.1007/s11548‑019‑02096‑931938993
     [Google Scholar]
  29. Almhdie-ImjabbarA. ToumiH. HarrarK. PintiA. LespessaillesE. Subchondral tibial bone texture of conventional X-rays predicts total knee arthroplasty.Sci. Rep.2022121832710.1038/s41598‑022‑12083‑x35585147
     [Google Scholar]
  30. JanvierT. JennaneR. ValeryA. HarrarK. DelplanqueM. LelongC. LoeuilleD. ToumiH. LespessaillesE. Subchondral tibial bone texture analysis predicts knee osteoarthritis progression: Data from the Osteoarthritis Initiative.Osteoarthritis Cartilage201725225926610.1016/j.joca.2016.10.00527742531
     [Google Scholar]
  31. YadavD.P. SharmaA. AthithanS. BholaA. SharmaB. DhaouI.B. Hybrid SFNet model for bone fracture detection and classification using ML/DL.Sensors.20222215582310.3390/s2215582335957380
     [Google Scholar]
  32. KimB. LeeD.W. LeeS. KoS. JoC. ParkJ. ChoiB.S. KrychA.J. PareekA. HanH.S. RoD.H. Automated detection of surgical implants on plain knee radiographs using a deep learning algorithm.Medicina.20225811167710.3390/medicina5811167736422216
     [Google Scholar]
  33. WangY. WangX. GaoT. DuL. WeiL. An automatic knee osteoarthritis diagnosis method based on deep learning: data from the osteoarthritis initiative.J. Healthc. Eng.2021202111010.1155/2021/4310648
     [Google Scholar]
  34. RonnebergerO. FischerP. BroxT. U-net: Convolutional networks for biomedical image segmentation.18th International ConferenceOctober 5-9, 2015Munich, Germany201523424110.1007/978‑3‑319‑24574‑4_28
     [Google Scholar]
  35. XiaoX. LianS. LuoZ. LiS. Weighted res-unet for high-quality retina vessel segmentation.2018 9th International Conference on Information Technology in Medicine and Education (ITME)19-21 October 2018Hangzhou, China201810.1109/ITME.2018.00080
     [Google Scholar]
  36. ShenW. XuW. ZhangH. SunZ. MaJ. MaX. ZhouS. GuoS. WangY. Automatic segmentation of the femur and tibia bones from X-ray images based on pure dilated residual U-Net.Inverse Probl. Imaging (Springfield)20211561333134610.3934/ipi.2020057
     [Google Scholar]
  37. OktayO. SchlemperJ. Le FolgocL. LeeM. HeinrichM. MisawaK. MoriK. Attention u-net: Learningwhere to look for the pancreas.arXiv:1804.039992018
     [Google Scholar]
  38. ZhaoC. XiangS. WangY. CaiZ. ShenJ. ZhouS. Di ZhaoW.S. GuoS. LiS. Context-aware network fusing transformer and V-net for semi-supervised segmentation of 3D left atrium.Expert Syst. Appl.2022214119105
     [Google Scholar]
  39. GoceriE. Evaluation of denoising techniques to remove speckle and Gaussian noise from dermoscopy images.Comput. Biol. Med.202315210647410.1016/j.compbiomed.2022.10647436563540
     [Google Scholar]
  40. GoceriE. Intensity normalization in brain MR images using spatially varying distribution matching.International Conferences Computer Graphics, Visualization, Computer Vision and Image Processing 20172017
     [Google Scholar]
  41. GoceriE. Fully automated and adaptive intensity normalization using statistical features for brain MR images.Celal Bayar Üniversitesi Fen Bilimleri Dergisi201814112513410.18466/cbayarfbe.384729
     [Google Scholar]
  42. GoceriE. Analysis of capsule networks for image classification.International conference on computer graphics, visualization, computer vision and image processing2021
     [Google Scholar]
  43. GoceriE. Capsule neural networks in classification of skin lesions.International Conferences Computer Graphics, Visualization, Computer Vision and Image Processing2021
     [Google Scholar]
  44. Han, Liang, Pin Tao, and Ralph R. Martin. "Livestock detection in aerial images using a fully convolutional network." Computational Visual Media 5 (2019): 221-228.
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056262464230922112606
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Deep Learning-based Automated Knee Joint Localization in Radiographic Images Using Faster R-CNN
Curr. Med. Imaging 20, e15734056262464 (2024); https://doi.org/10.2174/0115734056262464230922112606
/content/journals/cmir/10.2174/0115734056262464230922112606
/content/journals/cmir/10.2174/0115734056262464230922112606
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  • Article Type:     Research Article
Keyword(s): Deep learning; Faster R-CNN; Knee osteoarthritis; Object detection; RDN; X-ray images

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