膝骨關節炎是一種常見且對患者生活品質產生深遠影響的關節疾病，尤其對於老年人而言，常成為活動受限和殘疾的主要原因之一。評估膝骨關節炎的嚴重程度主要採用Kellgren-Lawrence分級系統，將嚴重程度從無症狀的第0級到最嚴重的第4級進行分級。但在臨床實踐中，醫師基於目視檢查的評分是主觀的，並且依賴醫師的大量經驗。因此，鑒於膝骨關節炎的高盛行率和臨床診斷的挑戰，深度學習技術被引入用於膝骨關節炎嚴重程度的評估，以提升分類的準確性和效率。然而，現有的膝骨關節炎分類模型往往僅聚焦於提高整體分類的準確性，而忽略了不同族群之間的公平性問題。
本研究使用OAI(Osteoarthritis Initiative)資料集作為膝骨關節炎平面X光攝影圖像分類研究的資料來源。由於醫學影像數據集資料數量相對有限，因此本研究提出以遷移學習為基礎，並微調模型以更好地學習圖像特徵，同時結合機器學習分類器，並使用簡化群體演算法優化機器學習分類器的超參數，以建立膝骨關節炎嚴重程度分類模型，旨在克服資料集數量相對稀少之問題，同時提升模型訓練效率與模型分類表現。為了評估本研究提出模型的公平性，以True Positive Rate差異作為評估模型在不同族群間表現差異的指標。接著，通過平衡訓練集各族群的樣本數，並使用重新平衡後的訓練集再次訓練與優化模型，以達到降低模型在不同族群間True Positive Rate差異之目標，確保本研究所提出之模型在各族群間實現更加公平的性能。

Knee osteoarthritis is a prevalent joint disease that significantly impacts the quality of life for affected individuals. Particularly among the elderly, it often becomes a leading cause of restricted mobility and disability. The severity of knee osteoarthritis is commonly assessed using the Kellgren-Lawrence grading system, which classifies severity from Grade 0 to Grade 4. However, in clinical practice, physician assessments based on visual inspections are subjective and reliant on the doctor's experience. Given the high prevalence of knee osteoarthritis and the challenges in clinical diagnosis, deep learning techniques have been introduced to enhance the accuracy and efficiency of severity classification. Nevertheless, existing knee osteoarthritis classification models tend to focus solely on improving overall classification accuracy, neglecting issues of fairness among different demographic groups.
This study utilizes data from the Osteoarthritis Initiative (OAI) as the data source for knee osteoarthritis classification research. This research proposes a method based on transfer learning, fine-tuning and integrating machine learning classifiers. Additionally, using the Simplified Swarm Optimization (SSO) algorithm to optimize the hyperparameters of the machine learning classifiers. The aim is to develop a model that overcomes the challenge of limited data, while enhancing the efficiency of model training and classification performance. To evaluate the fairness of the proposed model, the True Positive Rate disparity is employed as an indicator to evaluate performance disparities among different groups. Subsequently, the sample sizes of each group in the training set are balanced, and the model is retrained and optimized using the rebalanced training set. The goal is to reduce TPR disparities among different groups, ensuring that the proposed model achieves a more equitable performance across all groups.

[1] D. J. Hunter, L. March, and M. Chew, "Osteoarthritis in 2020 and beyond: a <i>Lancet</i> Commission," (in English), Lancet, Editorial Material vol. 396, no. 10264, pp. 1711-1712, Nov 2020, doi: 10.1016/s0140-6736(20)32230-3.
[2] D. J. Hunter and S. Bierma-Zeinstra, "Osteoarthritis," (in English), Lancet, Review vol. 393, no. 10182, pp. 1745-1759, Apr 2019, doi: 10.1016/s0140-6736(19)30417-9.
[3] L. Sharma, "Osteoarthritis of the Knee," (in English), N. Engl. J. Med., Article vol. 384, no. 1, pp. 51-59, Jan 2021, doi: 10.1056/NEJMcp1903768.
[4] J. W. J. Bijlsma, F. Berenbaum, and F. Lafeber, "Osteoarthritis: an update with relevance for clinical practice," (in English), Lancet, Article vol. 377, no. 9783, pp. 2115-2126, Jun 2011, doi: 10.1016/s0140-6736(11)60243-2.
[5] J. H. Kellgren and J. S. Lawrence, "RADIOLOGICAL ASSESSMENT OF OSTEO-ARTHROSIS," (in English), Ann. Rheum. Dis., Article vol. 16, no. 4, pp. 494-502, 1957, doi: 10.1136/ard.16.4.494.
[6] O. Sangha, "Epidemiology of rheumatic diseases," (in English), Rheumatology, Article; Proceedings Paper vol. 39, pp. 3-12, Dec 2000, doi: 10.1093/rheumatology/39.suppl_2.3.
[7] L. Shamir et al., "Knee X-Ray Image Analysis Method for Automated Detection of Osteoarthritis," (in English), IEEE Trans. Biomed. Eng., Article vol. 56, no. 2, pp. 407-415, Feb 2009, doi: 10.1109/tbme.2008.2006025.
[8] J. C. Buckland-Wright, D. G. Macfarlane, J. A. Lynch, M. K. Jasani, and C. R. Bradshaw, "Joint space width measures cartilage thickness in osteoarthritis of the knee: high resolution plain film and double contrast macroradiographic investigation," Ann. Rheum. Dis., vol. 54, no. 4, pp. 263-268, 1995, doi: 10.1136/ard.54.4.263.
[9] J. E. DACREE and E. C. HUSKISSON, "THE AUTOMATIC ASSESSMENT OF KNEE RADIOGRAPHS IN OSTEOARTHRITIS USING DIGITAL IMAGE ANALYSIS," Rheumatology, vol. 28, no. 6, pp. 506-510, 1989, doi: 10.1093/rheumatology/28.6.506.
[10] H. Oka et al., "Fully automatic quantification of knee osteoarthritis severity on plain radiographs," (in English), Osteoarthritis Cartilage, Article vol. 16, no. 11, pp. 1300-1306, Nov 2008, doi: 10.1016/j.joca.2008.03.011.
[11] T. Woloszynski, P. Podsiadlo, G. W. Stachowiak, and M. Kurzynski, "A signature dissimilarity measure for trabecular bone texture in knee radiographs," (in English), Med. Phys., Article vol. 37, no. 5, pp. 2030-2042, May 2010, doi: 10.1118/1.3373522.
[12] 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," (in English), Comput. Med. Imaging Graph., Article vol. 73, pp. 11-18, Apr 2019, doi: 10.1016/j.compmedimag.2019.01.007.
[13] D. A. Zebari, S. S. Sadiq, D. M. Sulaiman, and Ieee, "Knee Osteoarthritis Detection Using Deep Feature Based on Convolutional Neural Network," in 2nd International Conference on Computer Science and Software Engineering (CSASE), Univ Duhok, Duhok, IRAQ, Mar 15-17 2022, NEW YORK: Ieee, 2022, pp. 259-264, doi: 10.1109/csase51777.2022.9759799. [Online]. Available: <Go to ISI>://WOS:000851492300044
[14] A. Tiulpin, J. Thevenot, E. Rahtu, P. Lehenkari, and S. Saarakkala, "Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach," (in English), Sci Rep, Article vol. 8, p. 10, Jan 2018, Art no. 1727, doi: 10.1038/s41598-018-20132-7.
[15] S. S. Yadav and S. M. Jadhav, "Deep convolutional neural network based medical image classification for disease diagnosis," (in English), J. Big Data, Article vol. 6, no. 1, p. 18, Dec 2019, Art no. 113, doi: 10.1186/s40537-019-0276-2.
[16] H. C. Shin et al., "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning," (in English), IEEE Trans. Med. Imaging, Article vol. 35, no. 5, pp. 1285-1298, May 2016, doi: 10.1109/tmi.2016.2528162.
[17] J. Antony, K. McGuinness, N. E. O'Connor, K. Moran, and Ieee, "Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks," in 23rd International Conference on Pattern Recognition (ICPR), Mexican Assoc Comp Vis Robot & Neural Comp, Cancun, MEXICO, Dec 04-08 2016, LOS ALAMITOS: Ieee Computer Soc, in International Conference on Pattern Recognition, 2016, pp. 1195-1200. [Online]. Available: <Go to ISI>://WOS:000406771301034. [Online]. Available: <Go to ISI>://WOS:000406771301034
[18] J. Buolamwini and T. Gebru, "Gender shades: Intersectional accuracy disparities in commercial gender classification," in Conference on fairness, accountability and transparency, 2018: PMLR, pp. 77-91.
[19] J. Angwin, J. Larson, S. Mattu, and L. Kirchner, "Machine bias," in Ethics of data and analytics: Auerbach Publications, 2022, pp. 254-264.
[20] S. A. Saeed and R. M. Masters, "Disparities in Health Care and the Digital Divide," (in English), Curr. Psychiatry Rep., Review vol. 23, no. 9, p. 6, Sep 2021, Art no. 61, doi: 10.1007/s11920-021-01274-4.
[21] A. R. Martin, M. Kanai, Y. Kamatani, Y. Okada, B. M. Neale, and M. J. Daly, "Clinical use of current polygenic risk scores may exacerbate health disparities," (in English), Nature Genet., Article vol. 51, no. 4, pp. 584-591, Apr 2019, doi: 10.1038/s41588-019-0379-x.
[22] Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, "Dissecting racial bias in an algorithm used to manage the health of populations," (in English), Science, Article vol. 366, no. 6464, pp. 447-+, Oct 2019, Art no. aax2342, doi: 10.1126/science.aax2342.
[23] Microsoft. "Fate: Fairness, accountability, transparency, and ethics in ai." https://www.microsoft.com/en-us/research/theme/fate/ (accessed.
[24] E. Hine and L. Floridi, "The Blueprint for an AI Bill of Rights: In Search of Enaction, at Risk of Inaction," (in English), Minds Mach., Article vol. 33, no. 2, pp. 285-292, Jun 2023, doi: 10.1007/s11023-023-09625-1.
[25] Y. F. Wang et al., "Learning From Highly Confident Samples for Automatic Knee Osteoarthritis Severity Assessment: Data From the Osteoarthritis Initiative," (in English), IEEE J. Biomed. Health Inform., Article vol. 26, no. 3, pp. 1239-1250, Mar 2022, doi: 10.1109/jbhi.2021.3102090.
[26] Y. Nasser, R. Jennane, A. Chetouani, E. Lespessailles, and M. El Hassouni, "Discriminative Regularized Auto-Encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative," (in English), IEEE Trans. Med. Imaging, Article vol. 39, no. 9, pp. 2976-2984, Sept 2020, doi: 10.1109/tmi.2020.2985861.
[27] Y. Nasser, M. E. Hassouni, and R. Jennane, "Discriminative Deep Neural Network for Predicting Knee OsteoArthritis in Early Stage," in International Workshop on PRedictive Intelligence In MEdicine, 2022: Springer, pp. 126-136.
[28] L. C. Ribas, R. Riad, R. Jennane, and O. M. Bruno, "A complex network based approach for knee Osteoarthritis detection: Data from the Osteoarthritis initiative," (in English), Biomed. Signal Process. Control, Article vol. 71, p. 10, Jan 2022, Art no. 103133, doi: 10.1016/j.bspc.2021.103133.
[29] Z. Wang, A. Chetouani, D. Hans, E. Lespessailles, R. Jennane, and Ieee, "SIAMESE-GAP NETWORK FOR EARLY DETECTION OF KNEE OSTEOARTHRITIS," in 19th IEEE International Symposium on Biomedical Imaging (IEEE ISBI), Kolkata, INDIA, Mar 28-31 2022, NEW YORK: Ieee, in IEEE International Symposium on Biomedical Imaging, 2022, doi: 10.1109/isbi52829.2022.9761626. [Online]. Available: <Go to ISI>://WOS:000836243800224
[30] P. J. Chen, L. L. Gao, X. S. Shi, K. Allen, and L. Yang, "Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss," (in English), Comput. Med. Imaging Graph., Article vol. 75, pp. 84-92, Jul 2019, doi: 10.1016/j.compmedimag.2019.06.002.
[31] K. A. Thomas et al., "Automated classification of radiographic knee osteoarthritis severity using deep neural networks," Radiology: Artificial Intelligence, vol. 2, no. 2, p. e190065, 2020.
[32] A. Tiulpin and S. Saarakkala, "Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs Using Deep Convolutional Neural Networks," (in English), Diagnostics, Article vol. 10, no. 11, p. 12, Nov 2020, Art no. 932, doi: 10.3390/diagnostics10110932.
[33] A. Swiecicki et al., "Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists," (in English), Comput. Biol. Med., Article vol. 133, p. 9, Jun 2021, Art no. 104334, doi: 10.1016/j.compbiomed.2021.104334.
[34] E. Tat, D. L. Bhatt, and M. G. Rabbat, "Addressing bias: artificial intelligence in cardiovascular medicine," (in English), Lancet Digit. Health, Editorial Material vol. 2, no. 12, pp. E635-E636, Dec 2020. [Online]. Available: <Go to ISI>://WOS:000594484100004.
[35] P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, "AI in health and medicine," (in English), Nat. Med., Review vol. 28, no. 1, pp. 31-38, Jan 2022, doi: 10.1038/s41591-021-01614-0.
[36] S. N. Goodman, S. Goel, and M. R. Cullen, "Machine Learning, Health Disparities, and Causal Reasoning," (in English), Ann. Intern. Med., Editorial Material vol. 169, no. 12, pp. 883-+, Dec 2018, doi: 10.7326/m18-3297.
[37] A. Rajkomar, M. Hardt, M. D. Howell, G. Corrado, and M. H. Chin, "Ensuring Fairness in Machine Learning to Advance Health Equity," (in English), Ann. Intern. Med., Editorial Material vol. 169, no. 12, pp. 866-+, Dec 2018, doi: 10.7326/m18-1990.
[38] M. Klingenberg et al., "Higher performance for women than men in MRI-based Alzheimer′s disease detection," (in English), Alzheimers Res. Ther., Article vol. 15, no. 1, p. 13, Apr 2023, Art no. 84, doi: 10.1186/s13195-023-01225-6.
[39] L. Nordling, "A fairer way forward for AI in health care," (in eng), Nature, vol. 573, no. 7775, pp. S103-s105, Sep 2019, doi: 10.1038/d41586-019-02872-2.
[40] J. Chakraborty, S. Majumder, and T. Menzies, "Bias in Machine Learning Software: Why? How? What to Do?," in 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Electr Network, Aug 23-28 2021, NEW YORK: Assoc Computing Machinery, 2021, pp. 429-440, doi: 10.1145/3468264.3468537. [Online]. Available: <Go to ISI>://WOS:000744425500041
[41] A. J. Larrazabal, N. Nieto, V. Peterson, D. H. Milone, and E. Ferrante, "Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis," (in English), Proc. Natl. Acad. Sci. U. S. A., Article vol. 117, no. 23, pp. 12592-12594, Jun 2020, doi: 10.1073/pnas.1919012117.
[42] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, "A Survey on Bias and Fairness in Machine Learning," (in English), ACM Comput. Surv., Article vol. 54, no. 6, p. 35, Jul 2021, Art no. 115, doi: 10.1145/3457607.
[43] L. Seyyed-Kalantari, H. R. Zhang, M. B. A. McDermott, I. Y. Chen, and M. Ghassemi, "Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations," (in English), Nat. Med., Article vol. 27, no. 12, pp. 2176-+, Dec 2021, doi: 10.1038/s41591-021-01595-0.
[44] L. Seyyed-Kalantari, G. X. Liu, M. McDermott, I. Y. Chen, and M. Ghassemi, "CheXclusion: Fairness gaps in deep chest X-ray classifiers," in 26th Pacific Symposium on Biocomputing (PSB), Electr Network, Jan 05-07 2021, SINGAPORE: World Scientific Publ Co Pte Ltd, 2021, pp. 232-243. [Online]. Available: <Go to ISI>://WOS:000759784400022. [Online]. Available: <Go to ISI>://WOS:000759784400022
[45] R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," (in English), Insights Imaging, Review vol. 9, no. 4, pp. 611-629, Aug 2018, doi: 10.1007/s13244-018-0639-9.
[46] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," (in English), Proc. IEEE, Review vol. 86, no. 11, pp. 2278-2324, Nov 1998, doi: 10.1109/5.726791.
[47] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," (in English), Commun. ACM, Article vol. 60, no. 6, pp. 84-90, Jun 2017, doi: 10.1145/3065386.
[48] C. Szegedy et al., "Going Deeper with Convolutions," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, Jun 07-12 2015, NEW YORK: Ieee, in IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9, doi: 10.1109/cvpr.2015.7298594. [Online]. Available: <Go to ISI>://WOS:000387959200001
[49] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
[50] K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun, and Ieee, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, Jun 27-30 2016, NEW YORK: Ieee, in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778, doi: 10.1109/cvpr.2016.90. [Online]. Available: <Go to ISI>://WOS:000400012300083
[51] G. Huang, Z. Liu, L. van der Maaten, K. Q. Weinberger, and Ieee, "Densely Connected Convolutional Networks," in 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, Jul 21-26 2017, NEW YORK: Ieee, in IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2261-2269, doi: 10.1109/cvpr.2017.243. [Online]. Available: <Go to ISI>://WOS:000418371402035
[52] M. X. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," in 36th International Conference on Machine Learning (ICML), Long Beach, CA, Jun 09-15 2019, vol. 97, SAN DIEGO: Jmlr-Journal Machine Learning Research, in Proceedings of Machine Learning Research, 2019. [Online]. Available: <Go to ISI>://WOS:000684034306026. [Online]. Available: <Go to ISI>://WOS:000684034306026
[53] V. Gulshan et al., "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs," (in English), JAMA-J. Am. Med. Assoc., Article vol. 316, no. 22, pp. 2402-2410, Dec 2016, doi: 10.1001/jama.2016.17216.
[54] B. E. Bejnordi et al., "Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer," (in English), JAMA-J. Am. Med. Assoc., Article vol. 318, no. 22, pp. 2199-2210, Dec 2017, doi: 10.1001/jama.2017.14585.
[55] F. Chollet and Ieee, "Xception: Deep Learning with Depthwise Separable Convolutions," in 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, Jul 21-26 2017, NEW YORK: Ieee, in IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1800-1807, doi: 10.1109/cvpr.2017.195. [Online]. Available: <Go to ISI>://WOS:000418371401090
[56] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?," in 28th Conference on Neural Information Processing Systems (NIPS), Montreal, CANADA, Dec 08-13 2014, vol. 27, LA JOLLA: Neural Information Processing Systems (Nips), in Advances in Neural Information Processing Systems, 2014. [Online]. Available: <Go to ISI>://WOS:000452647101018. [Online]. Available: <Go to ISI>://WOS:000452647101018
[57] N. Tajbakhsh et al., "Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?," (in English), IEEE Trans. Med. Imaging, Article vol. 35, no. 5, pp. 1299-1312, May 2016, doi: 10.1109/tmi.2016.2535302.
[58] R. K. Zhang et al., "Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain," (in English), IEEE J. Biomed. Health Inform., Article vol. 21, no. 1, pp. 41-47, Jan 2017, doi: 10.1109/jbhi.2016.2635662.
[59] A. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks," (in English), Nature, Article vol. 542, no. 7639, pp. 115-+, Feb 2017, doi: 10.1038/nature21056.
[60] W. C. Yeh, "A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems," (in English), Expert Syst. Appl., Article vol. 36, no. 5, pp. 9192-9200, Jul 2009, doi: 10.1016/j.eswa.2008.12.024.
[61] J. Kennedy, R. Eberhart, Ieee, Ieee, Ieee, and Ieee, "Particle swarm optimization," in 1995 IEEE International Conference on Neural Networks (ICNN 95), Univ W Austraia, Perth, Australia, Nov 27-Dec 01 1995, NEW YORK: Ieee, 1995, pp. 1942-1948, doi: 10.1109/icnn.1995.488968. [Online]. Available: <Go to ISI>://WOS:A1995BF46H00374
[62] W. C. Yeh, "Orthogonal simplified swarm optimization for the series-parallel redundancy allocation problem with a mix of components," (in English), Knowledge-Based Syst., Article vol. 64, pp. 1-12, Jul 2014, doi: 10.1016/j.knosys.2014.03.011.
[63] C. L. Huang, "A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems," (in English), Reliab. Eng. Syst. Saf., Article vol. 142, pp. 221-230, Oct 2015, doi: 10.1016/j.ress.2015.06.002.
[64] J. H. Lee, W. C. Yeh, and M. C. Chuang, "Web page classification based on a simplified swarm optimization," (in English), Appl. Math. Comput., Article vol. 270, pp. 13-24, Nov 2015, doi: 10.1016/j.amc.2015.07.120.
[65] W. C. Yeh, "Novel swarm optimization for mining classification rules on thyroid gland data," (in English), Inf. Sci., Article vol. 197, pp. 65-76, Aug 2012, doi: 10.1016/j.ins.2012.02.009.
[66] W. C. Yeh, "New Parameter-Free Simplified Swarm Optimization for Artificial Neural Network Training and Its Application in the Prediction of Time Series," (in English), IEEE Trans. Neural Netw. Learn. Syst., Article vol. 24, no. 4, pp. 661-665, Apr 2013, doi: 10.1109/tnnls.2012.2232678.
[67] W. C. Yeh, Y. P. Lin, Y. C. Liang, C. M. Lai, and C. L. Huang, "Simplified swarm optimization for hyperparameters of convolutional neural networks," (in English), Comput. Ind. Eng., Article vol. 177, p. 11, Mar 2023, Art no. 109076, doi: 10.1016/j.cie.2023.109076.
[68] Y. K. Eyer, "Using simplified swarm optimization on path planning for intelligent mobile robot," in 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception (ICSCCW), Budapest, HUNGARY, Aug 22-25 2017, vol. 120, AMSTERDAM: Elsevier Science Bv, in Procedia Computer Science, 2017, pp. 83-90, doi: 10.1016/j.procs.2017.11.213. [Online]. Available: <Go to ISI>://WOS:000426703300014
[69] W. C. Yeh and S. Y. Tan, "Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function," (in English), Electronics, Article vol. 10, no. 15, p. 22, Aug 2021, Art no. 1775, doi: 10.3390/electronics10151775.
[70] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization," (in English), Int. J. Comput. Vis., Article vol. 128, no. 2, pp. 336-359, Feb 2020, doi: 10.1007/s11263-019-01228-7.
[71] 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 Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings 13, 2017: Springer, pp. 376-390.
[72] M. Górriz, J. Antony, K. McGuinness, X. Giró-i-Nieto, and N. E. O’Connor, "Assessing knee OA severity with CNN attention-based end-to-end architectures," in International conference on medical imaging with deep learning, 2019: PMLR, pp. 197-214.
[73] 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, p. 1380, 2023.
[74] 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," Journal of Healthcare Engineering, vol. 2021, no. 1, p. 5586529, 2021.
[75] Y. Feng, J. Liu, H. Zhang, and D. Qiu, "Automated grading of knee osteoarthritis X-ray images based on attention mechanism," in 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021: IEEE, pp. 1927-1932.