Knee Osteoarthritis Detection Using An Improved Centernet With Pixel-Wise Voting Scheme
Abstract
the main goal of the study is to discover and diagnose knee osteoarthritis from X-ray scanned knee pictures. Examining knee joint health using X-ray pictures is a frequent and reasonably priced approach; the research aims to use those pix precisely for osteoarthritis detection. Accuracy and precision of current image processing-based technologies for knee osteoarthritis detection present difficulties. The research aims to overcome the limitations of current methods by means of a unique and tailored methodology for greater detection and type of knee osteoarthritis, therefore addressing their deficiencies. The suggested method develops a state-of- the-art object identification architecture, a customised CenterNet. This CenterNet is designed with a pixel-wise voting system, which lets one extract features at a very best stage. This approach of customising the CenterNet seeks to improve the accuracy and dependability of knee osteoarthritis diagnosis. DenseNet 201 is included into the model as the feature extracting base network. DenseNet's highly linked layers—which encourage feature reuse and help to reduce gradient-related problems. Using DenseNet 201, the version seeks to maximise the most representative features from knee samples, hence strengthening the feature extraction process's robustness. The main purpose of the proposed model is to identify correct knee staining arthritis in X-ray images. Furthermore, the model uses Kelglen and Lawrence (KL) incremental methods to determine the degree of osteoarthritis and thus overcome detection. This all-encompassing approach guarantees a sophisticated knowledge of the condition, so supporting more efficient diagnosis and treatment planning. The project suggests an integrated strategy comprising efficient object detection strategies (YOLOv5, YOLOv8), powerful type models (Xception, InceptionV3), and a user-pleasant front end created with the Flask framework. This method seeks to apply the blessings of advanced type and detection models together with offering an ideal and secure testing environment.
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T. Tsonga, M. Michalopoulou, P. Malliou, G. Godolias, S. Kapetanakis, G. Gkasdaris, and P. Soucacos, ‘‘Analyzing the history of falls in patients with severe knee osteoarthritis,’’ Clinics Orthopedic Surg., vol. 7, no. 4, pp. 449–456, 2015.
B. J. E. de Lange-Brokaar, A. Ioan-Facsinay, E. Yusuf, A. W. Visser, H. M. Kroon, S. N. Andersen, L. Herb-van Toorn, G. J. V. M. van Osch, A.-M. Zuurmond, V. Stojanovic-Susulic, J. L. Bloem, R. G. H. H. Nelissen, T. W. J. Huizinga, and M. Kloppenburg, ‘‘Degree of synovitis on MRI by comprehensive whole knee semi-quantitative scoring method correlates with histologic and macroscopic features of synovial tissue inflammation in knee osteoarthritis,’’ Osteoarthritis Cartilage, vol. 22, no. 10, pp. 1606–1613, Oct. 2014.
C. Kokkotis, C. Ntakolia, S. Moustakidis, G. Giakas, and D. Tsaopoulos, ‘‘Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology,’’ Phys. Eng. Sci. Med., vol. 45, no. 1, pp. 219–229, Mar. 2022.
S. Nalband, R. R. Sreekrishna, and A. A. Prince, ‘‘Analysis of knee joint vibration signals using ensemble empirical mode decomposition,’’ Proc. Comput. Sci., vol. 89, pp. 820–827, Jan. 2016.
B. J. Guo, Z. L. Yang, and L. J. Zhang, ‘‘Gadolinium deposition in brain: Current scientific evidence and future perspectives,’’ Frontiers Mol. Neurosci., vol. 11, p. 335, Sep. 2018.
L. Shamir, S. M. Ling, W. W. Scott, A. Bos, N. Orlov, T. J. Macura, D. M. Eckley, L. Ferrucci, and I. G. Goldberg, ‘‘Knee X-ray image analysis method for automated detection of osteoarthritis,’’ IEEE Trans. Biomed. Eng., vol. 56, no. 2, pp. 407–415, Feb. 2009.
A. Brahim, R. Jennane, R. Riad, T. Janvier, L. Khedher, H. Toumi, and E. Lespessailles, ‘‘A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis initiative,’’ Comput. Med. Imag. Graph., vol. 73, pp. 11–18, Apr. 2019.
P. S. Emrani, J. N. Katz, C. L. Kessler, W. M. Reichmann, E. A. Wright, T. E. McAlindon, and E. Losina, ‘‘Joint space narrowing and Kellgren–Lawrence progression in knee osteoarthritis: An analytic literature synthesis,’’ Osteoarthritis Cartilage, vol. 16, no. 8, pp. 873–882, Aug. 2008.
M. N. Iqbal, F. R. Haidri, B. Motiani, and A. Mannan, ‘‘Frequency of factors associated with knee osteoarthritis,’’ J. Pakistan Med. Assoc., vol. 61, no. 8, p. 786, 2011.
A. Tiulpin, J. Thevenot, E. Rahtu, P. Lehenkari, and S. Saarakkala, ‘‘Automatic knee osteoarthritis diagnosis from plain radiographs: A deep learning-based approach,’’ Sci. Rep., vol. 8, no. 1, pp. 1–10, Jan. 2018.
M. S. M. Swamy and M. S. Holi, ‘‘Knee joint cartilage visualization and quantification in normal and osteoarthritis,’’ in Proc. Int. Conf. Syst. Med. Biol., Dec. 2010, pp. 138–142.
P. Dodin, J. Pelletier, J. Martel-Pelletier, and F. Abram, ‘‘Automatic human knee cartilage segmentation from 3-D magnetic resonance images,’’ IEEE Trans. Biomed. Eng., vol. 57, no. 11, pp. 2699–2711, Nov. 2010.
N. Kour, S. Gupta, and S. Arora, ‘‘A survey of knee osteoarthritis assessment based on gait,’’ Arch. Comput. Methods Eng., vol. 28, no. 2, pp. 345–385, Mar. 2021.
M. Saleem, M. S. Farid, S. Saleem, and M. H. Khan, ‘‘X-ray image analysis for automated knee osteoarthritis detection,’’ Signal, Image Video Process., vol. 14, no. 6, pp. 1079–1087, Sep. 2020.
J. Abedin, J. Antony, K. McGuinness, K. Moran, N. E. O’Connor, D. Rebholz-Schuhmann, and J. Newell, ‘‘Predicting knee osteoarthritis severity: Comparative modeling based on patient’s data and plain X-ray images,’’ Sci. Rep., vol. 9, no. 1, pp. 1–11, Apr. 2019.
J. Antony, K. McGuinness, K. Moran, and N. E. O’Connor, ‘‘Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks,’’ in Proc. 13th Int. Conf. Mach. Learn. Data Mining Pattern Recognit. (MLDM). New York, NY, USA: Springer, Jul. 2017, pp. 376–390.
J. Antony, K. McGuinness, N. E. O’Connor, and K. Moran, ‘‘Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks,’’ in Proc. 23rd Int. Conf. Pattern Recognit. (ICPR), Dec. 2016, pp. 1195–1200.
F. R. Mansour, ‘‘Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy,’’ Biomed. Eng. Lett., vol. 8, no. 1, pp. 41–57, Feb. 2018.
R. Mahum, S. U. Rehman, T. Meraj, H. T. Rauf, A. Irtaza, A. M. El-Sherbeeny, and M. A. El-Meligy, ‘‘A novel hybrid approach based on deep CNN features to detect knee osteoarthritis,’’ Sensors, vol. 21, no. 18, p. 6189, Sep. 2021.
C. Cernazanu-Glavan and S. Holban, ‘‘Segmentation of bone structure in X-ray images using convolutional neural network,’’ Adv. Elect. Comput. Eng., vol. 13, no. 1, pp. 87–94, 2013, doi: 10.4316/AECE.2013.01015.
M. Cabezas, A. Oliver, X. Lladó, J. Freixenet, and M. B. Cuadra, ‘‘A review of atlas-based segmentation for magnetic resonance brain images,’’ Comput. Methods Programs Biomed., vol. 104, no. 3, pp. e158– e177, Dec. 2011.
C. Stolojescu-Crişan and Ş. Holban, ‘‘A comparison of X-ray image segmentation techniques,’’ Adv. Electr. Comput. Eng., vol. 13, no. 3, pp. 85–92, 2013.
H. S. Gan and K. A. Sayuti, ‘‘Comparison of improved semi-automated segmentation technique with manual segmentation: Data from the osteoarthritis initiative,’’ Amer. J. Appl. Sci., vol. 13, no. 11, pp. 1068–1075, 2016, doi: 10.3844/ajassp.2016.1068.1075.
Y. Li, N. Xu, and Q. Lyu, ‘‘Construction of a knee osteoarthritis diagnostic system based on X-ray image processing,’’ Cluster Comput., vol. 22, no. S6, pp. 15533–15540, Nov. 2019.
S. Kubkaddi and K. Ravikumar, ‘‘Early detection of knee osteoarthritis using SVM classifier,’’ Int. J. Sci. Eng. Adv. Technol., vol. 5, no. 3, pp. 259–262, 2017.
Z. Zhu, X. He, G. Qi, Y. Li, B. Cong, and Y. Liu, ‘‘Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI,’’ Inf. Fusion, vol. 91, pp. 376–387, Mar. 2023.
A. Adegun and S. Viriri, ‘‘Deep learning techniques for skin lesion analysis and melanoma cancer detection: A survey of state-of-the-art,’’ Artif. Intell. Rev., vol. 54, no. 2 pp. 811–841, Jun. 2020.
M. A. Guillén, A. Llanes, B. Imbernón, R. Martínez-España, A. Bueno-Crespo, J.-C. Cano, and J. M. Cecilia, ‘‘Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning,’’ J. Supercomput., vol. 77, no. 1, pp. 818–840, Jan. 2021.
B. Janakiramaiah, G. Kalyani, and A. Jayalakshmi, ‘‘Retraction note: Automatic alert generation in a surveillance systems for smart city environment using deep learning algorithm,’’ Evol. Intell., vol. 14, no. 2, pp. 635–642, Dec. 2022.
A. F. M. Hani, A. S. Malik, D. Kumar, R. Kamil, R. Razak, and A. Kiflie, ‘‘Features and modalities for assessing early knee osteoarthritis,’’ in Proc. Int. Conf. Electr. Eng. Informat., Jul. 2011, pp. 1–6.
A. E. Nelson, F. Fang, L. Arbeeva, R. J. Cleveland, T. A. Schwartz, L. F. Callahan, J. S. Marron, and R. F. Loeser, ‘‘A machine learning approach to knee osteoarthritis phenotyping: Data from the FNIH biomarkers consortium,’’ Osteoarthritis Cartilage, vol. 27, no. 7, pp. 994–1001, Jul. 2019.
A. Aprovitola and L. Gallo, ‘‘Knee bone segmentation from MRI: A classification and literature review,’’ Biocybern. Biomed. Eng., vol. 36, no. 2, pp. 437–449, 2016.
V. Pedoia, S. Majumdar, and T. M. Link, ‘‘Segmentation of joint and musculoskeletal tissue in the study of arthritis,’’ Magn. Reson. Mater. Phys., Biol. Med., vol. 29, no. 2, pp. 207–221, Apr. 2016.
J. Kubicek, M. Penhaker, M. Augustynek, I. Bryjova, and M. Cerny, ‘‘Segmentation of knee cartilage: A comprehensive review,’’ J. Med. Imag. Health Informat., vol. 8, no. 3, pp. 401–418, Mar. 2018.
B. Zhang, Y. Zhang, H. D. Cheng, M. Xian, S. Gai, O. Cheng, and K. Huang, ‘‘Computer-aided knee joint magnetic resonance image segmentation—A survey,’’ 2018, arXiv:1802.04894.
T. Meena and S. Roy, ‘‘Bone fracture detection using deep supervised learning from radiological images: A paradigm shift,’’ Diagnostics, vol. 12, no. 10, p. 2420, Oct. 2022.
S. Roy, T. Meena, and S.-J. Lim, ‘‘Demystifying supervised learning in healthcare 4.0: A new reality of transforming diagnostic medicine,’’ Diagnostics, vol. 12, no. 10, p. 2549, Oct. 2022.
D. Pal, P. B. Reddy, and S. Roy, ‘‘Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray,’’ Comput. Biol. Med., vol. 150, Nov. 2022, Art. no. 106083.
H. Lee, H. Hong, and J. Kim, ‘‘BCD-NET: A novel method for cartilage segmentation of knee MRI via deep segmentation networks with bonecartilage-complex modeling,’’ in Proc. IEEE 15th Int. Symp. Biomed. Imag. (ISBI), Apr. 2018, pp. 1538–1541.
F. Liu, Z. Zhou, H. Jang, A. Samsonov, G. Zhao, and R. Kijowski, ‘‘Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging,’’ Magn. Reson. Med., vol. 79, no. 4, pp. 2379–2391, 2018.
Z. Zhou, G. Zhao, R. Kijowski, and F. Liu, ‘‘Deep convolutional neural network for segmentation of knee joint anatomy,’’ Magn. Reson. Med., vol. 80, no. 6, pp. 2759–2770, Dec. 2018.
R. Mahum, H. Munir, Z.-U.-N. Mughal, M. Awais, F. S. Khan, M. Saqlain, S. Mahamad, and I. Tlili, ‘‘A novel framework for potato leaf disease detection using an efficient deep learning model,’’ Hum. Ecol. Risk Assessment, Int. J., vol. 29, no. 2, pp. 303–326, Feb. 2023.
[43] S. Sikandar, R. Mahmum, and N. Akbar, ‘‘Cricket videos summary generation using a novel convolutional neural network,’’ in Proc. Mohammad Ali Jinnah Univ. Int. Conf. Comput. (MAJICC), Oct. 2022, pp. 1–7.
J. C.-W. Cheung, A. Y.-C. Tam, L.-C. Chan, P.-K. Chan, and C. Wen, ‘‘Superiority of multiple-joint space width over minimum-joint space width approach in the machine learning for radiographic severity and knee osteoarthritis progression,’’ Biology, vol. 10, no. 11, p. 1107, Oct. 2021.
A. Wahid, J. A. Shah, A. U. Khan, M. Ullah, and M. Z. Ayob, ‘‘Multilayered basis pursuit algorithms for classification of MR images of knee ACL tear,’’ IEEE Access, vol. 8, pp. 205424–205435, 2020, doi: 10.1109/ACCESS.2020.3037745.
Y. Wang, X. Wang, T. Gao, L. Du, and W. Liu, ‘‘An automatic knee osteoarthritis diagnosis method based on deep learning: Data from the osteoarthritis initiative,’’ J. Healthcare Eng., vol. 2021, pp. 1–10, Sep. 2021.
M. S. Swanson, J. W. Prescott, T. M. Best, K. Powell, R. D. Jackson, F. Haq, and M. N. Gurcan, ‘‘Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees,’’ Osteoarthritis Cartilage, vol. 18, no. 3, pp. 344–353, Mar. 2010.
H.-S. Gan, K. A. Sayuti, N. H. Harun, and A. H. A. Karim, ‘‘Flexible non cartilage seeds for osteoarthritic magnetic resoance image of knee: Data from the osteoarthritis initiative,’’ in Proc. IEEE EMBS Conf. Biomed. Eng. Sci. (IECBES), Dec. 2016, pp. 748–751.
S. Kashyap, H. Zhang, K. Rao, and M. Sonka, ‘‘Learning-based cost functions for 3-D and 4-D multi-surface multi-object segmentation of knee MRI: Data from the osteoarthritis initiative,’’ IEEE Trans. Med. Imag., vol. 37, no. 5, pp. 1103–1113, May 2018.
Q. Liu, Q. Wang, L. Zhang, Y. Gao, and D. Shen, ‘‘Multi-atlas context forests for knee MR image segmentation,’’ in Proc. 6th Int. Workshop Mach. Learn. Med. Imag. (MLMI). Munich, Germany: Springer, Oct. 2015, pp. 186–193.
S. S. Gornale, P. U. Patravali, A. M. Uppin, and P. S. Hiremath, ‘‘Study of segmentation techniques for assessment of osteoarthritis in knee X-ray images,’’ Int. J. Image, Graph. Signal Process., vol. 11, no. 2, pp. 48–57, Feb. 2019.
H.-S. Gan, K. A. Sayuti, M. H. Ramlee, Y.-S. Lee, W. M. H. W. Mahmud, and A. H. A. Karim, ‘‘Unifying the seeds auto-generation (SAGE) with knee cartilage segmentation framework: Data from the osteoarthritis initiative,’’ Int. J. Comput. Assist. Radiol. Surg., vol. 14, no. 5, pp. 755–762, May 2019.
J. H. Cueva, D. Castillo, H. Espinós-Morató, D. Durán, P. Díaz, and V. Lakshminarayanan, ‘‘Detection and classification of knee osteoarthritis,’’ Diagnostics, vol. 12, no. 10, p. 2362, Sep. 2022.
L. Anifah, M. H. Purnomo, T. L. R. Mengko, and I. K. E. Purnama, ‘‘Osteoarthritis severity determination using self organizing map based Gabor kernel,’’ IOP Conf. Ser., Mater. Sci. Eng., vol. 306, Feb. 2018, Art. no. 012071.
K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, ‘‘CenterNet: Keypoint triplets for object detection,’’ in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 6569–6578.
H. Law and J. Deng, ‘‘CornerNet: Detecting objects as paired keypoints,’’ in Proc. Eur. Conf. Comput. Vis. (ECCV), 2018, pp. 734–750.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, ‘‘Densely connected convolutional networks,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 4700–4708.
B. Xu, N. Wang, T. Chen, and M. Li, ‘‘Empirical evaluation of rectified activations in convolutional network,’’ 2015, arXiv:1505.00853.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, ‘‘Focal loss for dense object detection,’’ in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 2980–2988.
Y. Liu, K. Chen, C. Liu, Z. Qin, Z. Luo, and J. Wang, ‘‘Structured knowledge distillation for semantic segmentation,’’ in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 2604–2613.
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