Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

doi:10.3390/life12081126

. 2022 Jul 27;12(8):1126.

doi: 10.3390/life12081126.

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Usman Yunus¹, Javeria Amin², Muhammad Sharif¹, Mussarat Yasmin¹, Seifedine Kadry³, Sujatha Krishnamoorthy^{4

5}

Affiliations

¹ Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan.
² Department of Computer Science, University of Wah, Wah Cantt 47010, Pakistan.
³ Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway.
⁴ Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou 325060, China.
⁵ Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou 325060, China.

PMID: 36013305
PMCID: PMC9410095
DOI: 10.3390/life12081126

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Usman Yunus et al. Life (Basel). 2022.

. 2022 Jul 27;12(8):1126.

doi: 10.3390/life12081126.

Authors

Usman Yunus¹, Javeria Amin², Muhammad Sharif¹, Mussarat Yasmin¹, Seifedine Kadry³, Sujatha Krishnamoorthy^{4

5}

Affiliations

¹ Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan.
² Department of Computer Science, University of Wah, Wah Cantt 47010, Pakistan.
³ Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway.
⁴ Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou 325060, China.
⁵ Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou 325060, China.

PMID: 36013305
PMCID: PMC9410095
DOI: 10.3390/life12081126

Abstract

Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.

Keywords: KL grading; classification; features fusion; handcrafted features; knee osteoarthritis (KOA); localization.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

**Figure 1**
The architecture of the proposed methodology.

**Figure 2**
Graphical representation of LBP features.

**Figure 3**
Overview of feature extraction, selection, fusion, and classification.

**Figure 4**
Multi-class confusion matrix.

**Figure 5**
KOA localization results (a,c) original KOA slices (b,d) predicted scores (where G denotes grades).

See this image and copyright information in PMC

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References

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1. Murphy L., Helmick C.G. The impact of osteoarthritis in the United States: A population-health perspective: A population-based review of the fourth most common cause of hospitalization in US adults. Orthop. Nurs. 2012;31:85–91. doi: 10.1097/NOR.0b013e31824fcd42. - DOI - PubMed
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[1] Felson D.T., Naimark A., Anderson J., Kazis L., Castelli W., Meenan R.F. The prevalence of knee osteoarthritis in the elderly. The Framingham Osteoarthritis Study. Arthritis Rheum. Off. J. Am. Coll. Rheumatol. 1987;30:914–918. doi: 10.1002/art.1780300811. - DOI - PubMed

[2] Felson D.T., Naimark A., Anderson J., Kazis L., Castelli W., Meenan R.F. The prevalence of knee osteoarthritis in the elderly. The Framingham Osteoarthritis Study. Arthritis Rheum. Off. J. Am. Coll. Rheumatol. 1987;30:914–918. doi: 10.1002/art.1780300811. - DOI - PubMed

[3] Global Burden of Disease Study 2013 Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;286:743–800. - PMC - PubMed

[4] Global Burden of Disease Study 2013 Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;286:743–800. - PMC - PubMed

[5] Murphy L., Helmick C.G. The impact of osteoarthritis in the United States: A population-health perspective: A population-based review of the fourth most common cause of hospitalization in US adults. Orthop. Nurs. 2012;31:85–91. doi: 10.1097/NOR.0b013e31824fcd42. - DOI - PubMed

[6] Murphy L., Helmick C.G. The impact of osteoarthritis in the United States: A population-health perspective: A population-based review of the fourth most common cause of hospitalization in US adults. Orthop. Nurs. 2012;31:85–91. doi: 10.1097/NOR.0b013e31824fcd42. - DOI - PubMed

[7] Kurtz S., Ong K., Lau E., Mowat F., Halpern M. Proyecciones de artroplastia primaria y de revisión de cadera y rodilla en los Estados Unidos de 2005 a 2030. JBJS. 2007;89:780. doi: 10.2106/00004623-200704000-00012. - DOI

[8] Kurtz S., Ong K., Lau E., Mowat F., Halpern M. Proyecciones de artroplastia primaria y de revisión de cadera y rodilla en los Estados Unidos de 2005 a 2030. JBJS. 2007;89:780. doi: 10.2106/00004623-200704000-00012. - DOI

[9] Kaufman K.R., Hughes C., Morrey B.F., Morrey M., An K.-N. Gait characteristics of patients with knee osteoarthritis. J. Biomech. 2001;34:907–915. doi: 10.1016/S0021-9290(01)00036-7. - DOI - PubMed

[10] Kaufman K.R., Hughes C., Morrey B.F., Morrey M., An K.-N. Gait characteristics of patients with knee osteoarthritis. J. Biomech. 2001;34:907–915. doi: 10.1016/S0021-9290(01)00036-7. - DOI - PubMed

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Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Affiliations

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

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Abstract

Conflict of interest statement

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