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All issues Volume 328 (2025) EPJ Web Conf., 328 (2025) 01023 References
Open Access
Issue
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
Article Number 01023
Number of page(s) 13
DOI https://doi.org/10.1051/epjconf/202532801023
Published online 18 June 2025
  1. M.J. Lespasio, N.S. Piuzzi, M.E. Husni, G.F. Muschler, A. Guarino, and M.A. Mont, "Knee Osteoarthritis: A Primer," 2017. DOI: 10.7812/TPP/16-183. [Google Scholar]
  2. A. Cui, H. Li, D. Wang, J. Zhong, Y. Chen, and H. Lu, "Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies," EClinicalMedicine, vol. 29-30, Dec. 2020, DOI: 10.1016/j.eclinm.2020.100587. [Google Scholar]
  3. A. Courties, J. Sellam, and F. Berenbaum, "Metabolic syndrome-associated osteoarthritis," Curr Opin Rheumatol, vol. 29, no. 2, pp. 214–222, Mar. 2017, DOI: 10.1097/BOR.0000000000000373. [CrossRef] [PubMed] [Google Scholar]
  4. L. Li, "Deep residual autoencoder with multiscaling for semantic segmentation of land-use images," Remote Sens (Basel), vol. 11, no. 18, Sep. 2019, DOI: 10.3390/rs11182142. [Google Scholar]
  5. A. Saygili, "A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods," Appl Soft Comput, vol. 105, Jul. 2021, DOI: 10.1016/j.asoc.2021.107323. [CrossRef] [PubMed] [Google Scholar]
  6. M.K. Ha et al., "Comparative Analysis of Audio Processing Techniques on Doppler Radar Signature of Human Walking Motion Using CNN Models," Sensors (Basel), vol. 23, no. 21, Oct. 2023, DOI: 10.3390/s23218743. [Google Scholar]
  7. X. Huang, X. Han, S. Ma, T. Lin, and J. Gong, "Monitoring ecosystem service change in the City of Shenzhen by the use of high-resolution remotely sensed imagery and deep learning," Land Degrad Dev, vol. 30, no. 12, pp. 1490–1501, Jul. 2019, DOI: 10.1002/ldr.3337. [CrossRef] [Google Scholar]
  8. Y. Hapsari and Syamsuryadi, "Weather Classification Based on Hybrid Cloud Image Using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)," in Journal of Physics: Conference Series, Institute of Physics Publishing, Mar. 2019. DOI: 10.1088/1742-6596/1167/1/012064. [Google Scholar]
  9. L. Anifah, K.E. Purnama, M. Hariadi, and M.H. Purnomo, "Send Orders of Reprints at reprints@benthamscience.net 18 The Open," 2013. [Google Scholar]
  10. R.T. Wahyuningrum, L. Anifah, I.K.E. Purnama, and M.H. Purnomo, "A novel hybrid of S2DPCA and SVM for knee osteoarthritis classification," in 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), IEEE, Jun. 2016, pp. 1–5. DOI: 10.1109/CIVEMSA.2016.7524317. [Google Scholar]
  11. A.S. Mohammed, A.A. Hasanaath, G. Latif, and A. Bashar, "Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images," Diagnostics, vol. 13, no. 8, Apr. 2023, DOI: 10.3390/diagnostics13081380. [Google Scholar]
  12. C. Kokkotis, S. Moustakidis, E. Papageorgiou, G. Giakas, and D.E. Tsaopoulos, "Machine learning in knee osteoarthritis: A review," Sep. 01, 2020, Elsevier Ltd. DOI: 10.1016/j.ocarto.2020.100069. [Google Scholar]
  13. S.S. Gornale, P.U. Patravali, and R.R. Manza, "Detection of Osteoarthritis using Knee X-Ray Image Analyses: A Machine Vision based Approach," 2016. [Google Scholar]
  14. A. Brahim et al., "A Decision Support Tool For Early Detection of Knee OsteoArthritis using X-ray Imaging and Machine Learning: Data from the OsteoArthritis Initiative," 2019. [Google Scholar]
  15. S. Mehta, A. Gaur, and M.P. Sarathi, "A Simplified Method of Detection and Predicting the Severity of Knee Osteoarthritis," in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2023, pp. 1–7. DOI: 10.1109/ICCCNT56998.2023.10306649. [Google Scholar]
  16. R. Mahum et al., "A novel hybrid approach based on deep cnn features to detect knee osteoarthritis," Sensors, vol. 21, no. 18, Sep. 2021, DOI: 10.3390/s21186189. [CrossRef] [PubMed] [Google Scholar]
  17. N. Bayramoglu, A. Tiulpin, J. Hirvasniemi, M.T. Nieminen, and S. Saarakkala, "Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis," Osteoarthritis Cartilage, vol. 28, no. 7, pp. 941–952, Jul. 2020, DOI: 10.1016/j.joca.2020.03.006. [CrossRef] [PubMed] [Google Scholar]
  18. D.M. Belete and M.D. Huchaiah, "Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results," International Journal of Computers and Applications, vol. 44, no. 9, pp. 875–886, Sep. 2022, DOI: 10.1080/1206212X.2021.1974663. [CrossRef] [Google Scholar]
  19. T. Tariq, Z. Suhail, and Z. Nawaz, "Machine Learning Approaches for the Classification of Knee Osteoarthritis," in 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), IEEE, Jul. 2023, pp. 1–6. DOI: 10.1109/ICECCME57830.2023.10252236. [Google Scholar]
  20. C. Thornton, F. Hutter, H.H. Hoos, and K. Leyton-Brown, "Auto-WEKA," in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA: ACM, Aug. 2013, pp. 847–855. DOI: 10.1145/2487575.2487629. [Google Scholar]
  21. M. Kotti, L.D. Duffell, A.A. Faisal, and A.H. McGregor, "Detecting knee osteoarthritis and its discriminating parameters using random forests," Med Eng Phys, vol. 43, pp. 19–29, May 2017, DOI: 10.1016/j.medengphy.2017.02.004. [CrossRef] [PubMed] [Google Scholar]
  22. "Digital Knee X-ray Images-Mendeley Data." Accessed: Mar. 13, 2025. [Online]. Available: https://data.mendeley.com/datasets/t9ndx37v5h/1 [Google Scholar]
  23. N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), IEEE, pp. 886–893. DOI: 10.1109/CVPR.2005.177. [Google Scholar]
  24. S.S. Gornale, P.U. Patravali, K.S. Marathe, and P.S. Hiremath, "Determination of Osteoarthritis Using Histogram of Oriented Gradients and Multiclass SVM," International Journal of Image, Graphics and Signal Processing, vol. 9, no. 12, pp. 41–49, Dec. 2017, DOI: 10.5815/ijigsp.2017.12.05. [CrossRef] [Google Scholar]
  25. M. Huber, "Benchmark and survey of Automated Machine Learning Frameworks", Journal of Artifical Intelligence Research (2021) [Google Scholar]
  26. F. Hutter, L. Kotthoff, and J. Vanschoren, Eds., Automated Machine Learning. Cham: Springer International Publishing, 2019. DOI: 10.1007/978-3-030-05318-5. [Google Scholar]
  27. "Fine-Tuning the Model: What, Why, and How | by Amanatullah | Medium." Accessed: Mar. 13, 2025. [Online]. Available: https://medium.com/@amanatulla1606/fine-tuning-the-model-what-why-and-how-e7fa52bc8ddf [Google Scholar]
  28. R.K. Jain, P.K. Sharma, S. Gaj, A. Sur, and P. Ghosh, "Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network," Jun. 2021, [Online]. Available: http://arxiv.org/abs/2106.14292 [Google Scholar]
  29. A. Swiecicki et al., "Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists," Comput Biol Med, vol. 133, p. 104334, Jun. 2021, DOI: 10.1016/j.compbiomed.2021.104334. [CrossRef] [PubMed] [Google Scholar]
  30. S. Olsson, E. Akbarian, A. Lind, A.S. Razavian, and M. Gordon, "Automating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population," BMC Musculoskelet Disord, vol. 22, no. 1, Dec. 2021, DOI: 10.1186/s12891-021-04722-7. [CrossRef] [Google Scholar]
  31. B. Zhang, J. Tan, K. Cho, G. Chang, and C.M. Deniz, "Attention-based CNN for KL Grade Classification: Data from the Osteoarthritis Initiative," in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), IEEE, Apr. 2020, pp. 731–735. DOI: 10.1109/ISBI45749.2020.909 [CrossRef] [Google Scholar]

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