Phan, T.H., Nguyen, T.S., Nguyen, T.T., Le, T.L., Mai, D.T., Quan, T.T.: Diko: A two-stage hybrid network for knee osteoarthritis diagnosis using deep learning. In: Intelligence of Things: Technologies and Applications, vol. 25, pp. 360–369 (2023)
Cui, A., Li, H., Wang, D., Zhong, J., Chen, Y., Lu, H.: Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies. EClinicalMedicine. 29-30, 100587 (2020)
Article PubMed PubMed Central Google Scholar
Ho-Pham, L.T., Lai, T.Q., Mai, L.D., Doan, M.C., Pham, H.N., Nguyen, T.V.: Prevalence of radiographic osteoarthritis of the knee and its relationship to self-reported pain. PloS One. 9(4), 94563 (2014)
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M.A.A., Dwivedi, Y.K.: A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge. 8(1), 100333 (2023)
Wahyuningrum, R.T., Anifah, L., Purnama, I.K.E., Purnomo, M.H.: A novel hybrid of s2dpca and svm for knee osteoarthritis classification. In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 1–5 (2016)
Aprilliani, U., Rustam, Z.: Osteoarthritis disease prediction based on random forest. In: IEEE International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 237–240 (2018)
Wang, Y., Li, S., Zhao, B., Zhang, J., Yang, Y., Li, B.: A resnet-based approach for accurate radiographic diagnosis of knee osteoarthritis. CAAI Transactions on Intelligence Technology. 7(3), 512–521 (2022)
Górriz, M., Antony, J., McGuinness, K., Giró-i-Nieto, X., O’Connor, N.: Assessing knee oa severity with cnn attention-based end-to-end architectures. In: Proceedings of The International Conference on Medical Imaging with Deep Learning, vol. 102, pp. 197–214 (2019)
Kellgren, J.H., Lawrence, J.: Radiological assessment of osteo-arthrosis. Annals of The Rheumatic Diseases. 16(4), 494 (1957)
Article CAS PubMed PubMed Central Google Scholar
Antony, J., McGuinness, K., Moran, K., O’Connor, N.E.: Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks. In: Machine Learning and Data Mining in Pattern Recognition, pp. 376–390 (2017)
Swiecicki, A., Li, N., O’Donnell, J., Said, N., Yang, J., Mather, R.C., Jiranek, W.A., Mazurowski, M.A.: Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. Computers in Biology and Medicine. 133, 104334 (2021)
Chen, P., Gao, L., Shi, X., Allen, K., Yang, L.: Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Computerized Medical Imaging and Graphics. 75, 84–92 (2019)
Article PubMed PubMed Central Google Scholar
Cueva, J.H., Castillo, D., Espinós-Morató, H., Durán, D., Díaz, P., Lakshminarayanan, V.: Detection and classification of knee osteoarthritis. Diagnostics. 12(10), 2362 (2022)
Article PubMed PubMed Central Google Scholar
Liu, L., Chang, J., Zhang, P., Ma, Q., Zhang, H., Sun, T., Qiao, H.: A joint multi-modal learning method for early-stage knee osteoarthritis disease classification. Heliyon. 9(4), 15461 (2023)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision - ECCV, pp. 21–37 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 28 (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Lindner, C., Thiagarajah, S., Wilkinson, J.M., Wallis, G.A., Cootes, T.F., Consortium, et al.: Fully automatic segmentation of the proximal femur using random forest regression voting. IEEE Transactions on Medical Imaging. 32(8), 1462–1472 (2013)
Tiulpin, A., Melekhov, I., Saarakkala, S.: Kneel: Knee anatomical landmark localization using hourglass networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR), pp. 1–14 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation (2015). arXiv:1411.4038
Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S.: Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Scientific Reports. 8, 1727 (2018)
Article PubMed PubMed Central Google Scholar
Zhu, L., Han, J., Guo, R., Wu, D., Wei, Q., Chai, W., Tang, S.: An automatic classification of the early osteonecrosis of femoral head with deep learning. Current Medical Imaging. 16(10), 1323–1331 (2020)
Deng, Y., Wang, L., Zhao, C., Tang, S., Cheng, X., Deng, H.-W., Zhou, W.: A deep learning-based approach to automatic proximal femur segmentation in quantitative ct images. Medical & Biological Engineering & Computing. 60(5), 1417–1429 (2022)
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) (2021)
Alshareef, E.A., Ebrahim, F.O., Lamami, Y., Milad, M.B., Eswani, M.S., Bashir, S.A., Bshina, S.A., Jakdoum, A., Abourqeeqah, A., Elbasir, M.O., Elbahrit, E.A.: Knee osteoarthritis severity grading using vision transformer. Journal of Intelligent & Fuzzy Systems. 43, 1–11 (2022)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). arXiv:1804.02767
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(9), 1904–1916 (2015)
Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: optimal speed and accuracy of object detection (2020). arXiv:2004.10934
Wang, C.-Y., Mark Liao, H.-Y., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H.: Cspnet: A new backbone that can enhance learning capability of cnn. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1571–1580 (2020)
Misra, D.: Mish: A Self Regularized Non-Monotonic Activation Function (2020). arXiv:1908.08681
Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks. 107, 3–11 (2018)
Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV, pp. 474–485 (1994)
LaValle, S.M., Branicky, M.S., Lindemann, S.R.: On the relationship between classical grid search and probabilistic roadmaps. The International Journal of Robotics Research. 23(7-8), 673–692 (2004)
Huang, G., Liu, Z., Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)
Tan, M., Le, Q.: Efficientnetv2: Smaller models and faster training. In: Proceedings of the 38th International Conference on Machine Learning, vol. 139, pp. 10096–10106 (2021)
Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks. 106, 249–259 (2018)
Chen, P., Liu, S., Zhao, H., Wang, X., Jia, J.: Gridmask data augmentation (2020). arXiv:2001.04086
Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: Yolov9: learning what you want to learn using programmable gradient information (2024). arXiv:2402.13616
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: Yolov10: real-time end-to-end object detection (2024). arXiv:2405.14458
Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: Detrs beat yolos on real-time object detection (2024). arXiv:2304.08069
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1) (2017)
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (CVPR), pp. 10012–10022 (2021)
Tiulpin, A., Klein, S., Bierma-Zeinstra, S.M., Thevenot, J., Rahtu, E., Meurs, J.v., Oei, E.H., Saarakkala, S.: Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Scientific reports. 9(1), 20038 (2019)
Wang, Y., Wang, X., Gao, T., Du, L., Liu, W.: An automatic knee osteoarthritis diagnosis method based on deep learning: Data from the osteoarthritis initiative. Journal of Healthcare Engineering. 2021(1), 5586529 (2021)
PubMed PubMed Central Google Scholar
Sekhri, A., Kerkouri, M.A., Chetouani, A., Tliba, M., Nasser, Y., Jennane, R., Bruno, A.: Automatic diagnosis of knee osteoarthritis severity using swin transformer. In: Proceedings of the 20th International Conference on Content-Based Multimedia Indexing, pp. 41–47 (2023)
Jain, R.K., Sharma, P.K., Gaj, S., Sur, A., Ghosh, P.: Knee osteoarthritis severity prediction using an attentive multi-scale deep convolutional neural network. Multimedia Tools and Applications. 83, 1–18 (2023)
留言 (0)