{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T23:12:00Z","timestamp":1791241920509,"version":"4.1.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100003008","name":"Ministry of Higher Education, Egypt","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003008","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-024-00615-4","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T07:05:24Z","timestamp":1726124724000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Knee Osteoporosis Diagnosis Based on Deep Learning"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0151-9619","authenticated-orcid":false,"given":"Amany M.","family":"Sarhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0060-0134","authenticated-orcid":false,"given":"Mohamed","family":"Gobara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5857-3938","authenticated-orcid":false,"given":"Shady","family":"Yasser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2833-9152","authenticated-orcid":false,"given":"Zainab","family":"Elsayed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2968-2545","authenticated-orcid":false,"given":"Ghada","family":"Sherif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0921-1621","authenticated-orcid":false,"given":"Nada","family":"Moataz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0604-3402","authenticated-orcid":false,"given":"Yasmen","family":"Yasir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7119-5749","authenticated-orcid":false,"given":"Esraa","family":"Moustafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7657-7139","authenticated-orcid":false,"given":"Sara","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hesham A.","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"issue":"6","key":"615_CR1","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/BF01623184","volume":"2","author":"C Cooper","year":"1992","unstructured":"Cooper, C., Campion, G., Melton, L.: Hip fractures in the elderly: a world-wide projection. Osteoporos Int. 2(6), 285\u2013289 (1992)","journal-title":"Osteoporos Int."},{"key":"615_CR2","unstructured":"https:\/\/www.who.int\/ar\/data\/gho\/publications\/world-health-statistics"},{"key":"615_CR3","unstructured":"https:\/\/www.Osteoporosis.foundation\/wod2023-survey"},{"key":"615_CR4","doi-asserted-by":"crossref","unstructured":"Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: \u201cMedical image classification with convolutional neural network,\" Proceedings of the IEEE 13th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 844\u2013848, (2014)","DOI":"10.1109\/ICARCV.2014.7064414"},{"issue":"1","key":"615_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0276-2","volume":"6","author":"SS Yadav","year":"2019","unstructured":"Yadav, S.S., Jadhav, S.M.: Deep convolutional neural network based medical image classification for disease diagnosis. J. Big Data 6(1), 1\u201318 (2019)","journal-title":"J. Big Data"},{"issue":"4","key":"615_CR6","doi-asserted-by":"publisher","first-page":"176","DOI":"10.3390\/a16040176","volume":"16","author":"I Mahmud","year":"2023","unstructured":"Mahmud, I., Mamun, M., Abdelgawad, A.: A deep analysis of brain tumor detection from mr images using deep learning networks. Algorithms 16(4), 176 (2023). https:\/\/doi.org\/10.3390\/a16040176","journal-title":"Algorithms"},{"key":"615_CR7","first-page":"1","volume-title":"Multimedia Tools and Applications","author":"DA Arafa","year":"2022","unstructured":"Arafa, D.A., Moustafa, H.E.D., Ali, H.A., Ali-Eldin, A.M.T., Saraya, S.: A deep learning framework for early diagnosis of Alzheimer\u2019s disease on MRI images. In: Multimedia Tools and Applications, pp. 1\u201333. Springer, Cham (2022)"},{"key":"615_CR8","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.artmed.2021.102156","volume":"119","author":"HM Balaha","year":"2021","unstructured":"Balaha, H.M., Balaha, M.H., Ali, H.A.: Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms. Artif. Intell. Med. 119, 102\u2013156 (2021)","journal-title":"Artif. Intell. Med."},{"key":"615_CR9","first-page":"1","volume-title":"Neural computing and applications","author":"E Shalaby","year":"2022","unstructured":"Shalaby, E., ElShennawy, N., Sarhan, A.: Utilizing deep learning models in CSI-based human activity recognition. In: Neural computing and applications, pp. 1\u201318. Springer, Cham (2022)"},{"issue":"1","key":"615_CR10","doi-asserted-by":"publisher","first-page":"245","DOI":"10.5755\/j01.itc.52.1.32323","volume":"52","author":"S Sathyavathi","year":"2023","unstructured":"Sathyavathi, S., Baskaran, K.R.: An intelligent human age prediction from face image framework based on deep learning algorithms. Inform. Technol. Control 52(1), 245\u2013257 (2023)","journal-title":"Inform. Technol. Control"},{"issue":"2","key":"615_CR11","doi-asserted-by":"publisher","first-page":"392","DOI":"10.3390\/jcm9020392","volume":"9","author":"K Lee","year":"2020","unstructured":"Lee, K., Jung, S.K., Ryu, J.J., Shin, S.W., Choi, J.: Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J. Clin. Med. 9(2), 392 (2020)","journal-title":"J. Clin. Med."},{"issue":"1","key":"615_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.3390\/app14010133","volume":"14","author":"S Feng","year":"2023","unstructured":"Feng, S., Lin, S., Chiang, Y., Lu, M., Chao, Y.: Deep learning-based hip X-ray image analysis for predicting osteoporosis. Appl. Sci. 14(1), 133 (2023)","journal-title":"Appl. Sci."},{"key":"615_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2020.115561","volume":"140","author":"B Zhang","year":"2020","unstructured":"Zhang, B., Yu, K., Ning, Z., Wang, K., Dong, Y., Liu, X., Liu, S., Wang, J., Zhu, C., Yu, Q., Duan, Y.: Deep learning of lumbar spine X-ray for osteopenia and Osteoporosis screening: a multicenter retrospective cohort study. Bone 140, 115561 (2020)","journal-title":"Bone"},{"issue":"20","key":"615_CR14","doi-asserted-by":"publisher","first-page":"11150","DOI":"10.3390\/app132011150","volume":"13","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Zheng, H., Duan, J., Wang, X.: GLCM-based FBLS: a novel broad learning system for knee osteopenia and osteoporosis screening in athletes. Appl. Sci., MDPI 13(20), 11150 (2023)","journal-title":"Appl. Sci., MDPI"},{"issue":"3","key":"615_CR15","doi-asserted-by":"publisher","first-page":"691","DOI":"10.3390\/diagnostics12030691","volume":"12","author":"R Sebro","year":"2022","unstructured":"Sebro, R., De la Garza-Ramos, C.: Machine learning for opportunistic screening for Osteoporosis from CT scans of the wrist and forearm. Diagnostics, MDPI 12(3), 691 (2022)","journal-title":"Diagnostics, MDPI"},{"issue":"8","key":"615_CR16","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3991\/ijoe.v19i08.39235","volume":"19","author":"PS Dodamani","year":"2023","unstructured":"Dodamani, P.S., Danti, A.: Transfer learning-based osteoporosis classification using simple radiographs. Int. J. Onl. Biomed. Eng. 19(8), 66 (2023)","journal-title":"Int. J. Onl. Biomed. Eng."},{"issue":"9","key":"615_CR17","doi-asserted-by":"publisher","first-page":"14193","DOI":"10.1007\/s11042-022-13911-y","volume":"82","author":"MI Wani","year":"2023","unstructured":"Wani, M.I., Arora, S.: Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network. Multim. Tools Appl. 82(9), 14193\u201314217 (2023)","journal-title":"Multim. Tools Appl."},{"key":"615_CR18","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2023.0140430","author":"S Kumar","year":"2023","unstructured":"Kumar, S., Goswami, P., Batra, S.: Fuzzy rank-based ensemble model for accurate diagnosis of osteoporosis in knee radiographs. IJACSA Int. J. Adv. Comput. Sci. Appl. (2023). https:\/\/doi.org\/10.14569\/IJACSA.2023.0140430","journal-title":"IJACSA Int. J. Adv. Comput. Sci. Appl."},{"key":"615_CR19","doi-asserted-by":"publisher","DOI":"10.18201\/ijisae.2021473646","author":"MM Ashames","year":"2021","unstructured":"Ashames, M.M., Ceylan, M., Jennane, R.: Deep transfer learning and majority voting approaches for Osteoporosis classification. Int. J. Intell. Syst. Appl. Eng. (2021). https:\/\/doi.org\/10.18201\/ijisae.2021473646","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"issue":"21","key":"615_CR20","doi-asserted-by":"publisher","first-page":"8189","DOI":"10.3390\/s22218189","volume":"22","author":"R Dzier\u017cak","year":"2022","unstructured":"Dzier\u017cak, R., Omiotek, Z.: Application of deep convolutional neural networks in the diagnosis of Osteoporosis. Sens., MDPI 22(21), 8189 (2022)","journal-title":"Sens., MDPI"},{"key":"615_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.37394\/232017.2022.13.7","volume":"13","author":"UB Abubakar","year":"2022","unstructured":"Abubakar, U.B., Boukar, M.M., Adeshina, S., Dane, S.: Transfer learning model training time comparison for Osteoporosis classification on knee radiograph of RGB and grayscale images. WSEAS Trans. Electron. 13, 45\u201351 (2022)","journal-title":"WSEAS Trans. Electron."},{"key":"615_CR22","unstructured":"https:\/\/kaggle.com\/datasets\/866059b7930a5c49cd77d94c1761840a19d88074cad74e8f0e0cfa2b236a6904"},{"key":"615_CR23","unstructured":"https:\/\/www.kaggle.com\/datasets\/mrmann007\/Osteoporosis"},{"key":"615_CR24","unstructured":"https:\/\/www.kaggle.com\/datasets\/sachinkumar413\/Osteoporosis-knee-dataset-preprocessed128x256"},{"key":"615_CR25","unstructured":"https:\/\/www.kaggle.com\/datasets\/stevepython\/Osteoporosis-knee-xray-dataset"},{"key":"615_CR26","unstructured":"https:\/\/data.mendeley.com\/datasets\/fxjm8fb6mw\/2"},{"key":"615_CR27","unstructured":"Yang, T.S.: \"Recognition and classification of knee osteoporosis and osteoarthritis severity using deep learning techniques,\" (Doctoral dissertation, Dublin, National College of Ireland), (2022)"},{"key":"615_CR28","doi-asserted-by":"crossref","unstructured":"Vishnu, T., Saranya, K., Arunkumar, R., Devi, M.G.,:\"Efficient and early detection of Osteoporosis using trabecular region,\" Proceedings of the IEEE Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1\u20135, (2015)","DOI":"10.1109\/GET.2015.7453840"},{"key":"615_CR29","unstructured":"Bengio, Y.: \"Deep learning of representations for unsupervised and transfer learning,\" Proceedings of ICML workshop on unsupervised and transfer learning, pp. 17\u201336, (2012)"},{"issue":"5","key":"615_CR30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0217293","volume":"14","author":"KM Hosny","year":"2019","unstructured":"Hosny, K.M., Kassem, M.A., Foaud, M.M.: Classification of skin lesions using transfer learning and augmentation with Alexnet. PLoS ONE 14(5), e0217293 (2019)","journal-title":"PLoS ONE"},{"key":"615_CR31","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 25, 1097\u20131105 (2012)","journal-title":"Commun. ACM"},{"key":"615_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05082-4","author":"S Lu","year":"2020","unstructured":"Lu, S., Wang, S.-H., Zhang, Y.-D.: Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Appl. MDPI (2020). https:\/\/doi.org\/10.1007\/s00521-020-05082-4","journal-title":"Appl. MDPI"},{"key":"615_CR33","doi-asserted-by":"publisher","DOI":"10.24017\/covid.14","author":"SQ Salih","year":"2020","unstructured":"Salih, S.Q., Hawre, Kh., et al.: \"Modified Alexnet convolution neural network for Covid-19 detection using chest X-ray images. KJAR (2020). https:\/\/doi.org\/10.24017\/covid.14","journal-title":"KJAR"},{"key":"615_CR34","doi-asserted-by":"crossref","unstructured":"Guo, M., Du, Y.: \"Classification of thyroid ultrasound standard plane images using ResNet-18 Networks,\" Proceedings of the IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), pp. 324\u2013328, (2019)","DOI":"10.1109\/ICASID.2019.8925267"},{"key":"615_CR35","doi-asserted-by":"crossref","unstructured":"He, K, Zhang, X, Ren, S, Sun, J.: \"Deep residual learning for image recognition,\" Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778, (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"615_CR36","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/978-3-030-36708-4_44","volume-title":"Proceedings of the International Conference on Neural Information Processing","author":"D Liu","year":"2019","unstructured":"Liu, D., Liu, Y., Dong, L.: G-ResNet: Improved ResNet for brain tumor classification. In: Proceedings of the International Conference on Neural Information Processing, pp. 535\u2013545. Springer, Cham (2019)"},{"issue":"2","key":"615_CR37","first-page":"219","volume":"168","author":"X Yu","year":"2019","unstructured":"Yu, X., Wang, S.-H.: Abnormality diagnosis in mammograms by transfer learning based on ResNet18. Fund. Inform. 168(2), 219\u2013230 (2019)","journal-title":"Fund. Inform."},{"key":"615_CR38","doi-asserted-by":"crossref","unstructured":"Ebrahimi-Ghahnavieh, A., Luo, S., Chiong, R.: \"Transfer learning for Alzheimer's disease detection on MRI images,\" Proceedings of the IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), pp. 133\u2013138, (2019)","DOI":"10.1109\/ICIAICT.2019.8784845"},{"issue":"2","key":"615_CR39","first-page":"108","volume":"11","author":"J Howard","year":"2020","unstructured":"Howard, J., Gugger, S.: A layered API for deep learning. Inform., MDPI 11(2), 108 (2020)","journal-title":"Inform., MDPI"},{"key":"615_CR40","doi-asserted-by":"publisher","first-page":"61408","DOI":"10.1109\/ACCESS.2021.3074422","volume":"9","author":"Z Khan","year":"2021","unstructured":"Khan, Z., Khan, F.G., Khan, A., Rehman, Z., Shah, S., Qummar, S., Pack, S.: Diabetic retinopathy detection using VGG-NIN a deep learning architecture. IEEE Access 9, 61408\u201361416 (2021)","journal-title":"IEEE Access"},{"key":"615_CR41","doi-asserted-by":"crossref","unstructured":"Militante, S.V.: \"Malaria disease recognition through adaptive deep learning models of convolutional neural network,\" Proceedings of the IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS), pp. 1\u20136, (2019)","DOI":"10.1109\/ICETAS48360.2019.9117332"},{"key":"615_CR42","unstructured":"Simonyan, K., Zisserman, A.: \"Very deep convolutional networks for large-scale image recognition,\" arXiv preprint arXiv:1409.1556, (2014)"},{"issue":"8","key":"615_CR43","doi-asserted-by":"publisher","first-page":"9836","DOI":"10.1109\/TPAMI.2023.3256421","volume":"45","author":"O Tutsoy","year":"2023","unstructured":"Tutsoy, O.: Graph theory based large-scale machine learning with multi-dimensional constrained optimization approaches for exact epidemiological modeling of pandemic diseases. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 9836\u20139845 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3256421","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00615-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00615-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00615-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T07:11:06Z","timestamp":1726125066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00615-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,12]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["615"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00615-4","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,12]]},"assertion":[{"value":"9 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"241"}}