{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T16:05:33Z","timestamp":1786464333766,"version":"build-2736575974"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T00:00:00Z","timestamp":1650931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>The practice of Deep Convolution neural networks in the field of medicine has congregated immense success and significance in present situations. Previously, researchers have developed numerous models for detecting abnormalities in musculoskeletal radiographs of upper extremities, but did not succeed in achieving respectable accuracy in the case of finger radiographs. A novel deep neural network-based hybrid architecture named ComDNet-512 is proposed in this paper to efficiently detect the bone abnormalities in the musculoskeletal radiograph of a patient. ComDNet-512 comprises a three-phase pipeline structure: compression, training of the dense neural network, and progressive resizing. The ComDNet-512 hybrid model is trained with finger radiographs samples to make a binary prediction, i.e., normal or abnormal bones. The proposed model showed phenomenon outcomes when cross-validated on the testing samples of arthritis patients and gives many superior results when compared with state-of-the-art practices. The model is able to achieve an area under the ROC curve (AUC) equal to 0.894 (sensitivity = 0.941 and specificity = 0.847). The Precision, Recall, F1 Score, and Kappa values, recorded as 0.86, 0.94, 0.89, and 0.78, respectively, are better than any of the previous models\u2019. With an increasing appearance of enormous cases of musculoskeletal conditions in people, deep learning-based computational solutions can play a big role in performing automated detections in the future.<\/jats:p>","DOI":"10.3390\/biology11050665","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T11:45:55Z","timestamp":1650973555000},"page":"665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Hybrid Deep Learning Approach for Automatic Detection in Musculoskeletal Radiographs"],"prefix":"10.3390","volume":"11","author":[{"given":"Gurpreet","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-0320","authenticated-orcid":false,"given":"Darpan","family":"Anand","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9603-8673","authenticated-orcid":false,"given":"Woong","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Software Convergence, Daegu Catholic University, Gyeongsan 38430, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5446-288X","authenticated-orcid":false,"given":"Gyanendra Prasad","family":"Joshi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5795-1075","authenticated-orcid":false,"given":"Kwang Chul","family":"Son","sequence":"additional","affiliation":[{"name":"Department of Information Contents, Kwangwoon University, Seoul 01897, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.2307\/3527467","article-title":"The WHO definition of health","volume":"1","author":"Callahan","year":"1973","journal-title":"Hastings Cent. Stud."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1093\/neuros\/nyx384","article-title":"Natural and artificial intelligence in neurosurgery: A systematic review","volume":"83","author":"Senders","year":"2018","journal-title":"Neurosurgery"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s13244-016-0534-1","article-title":"Error and discrepancy in radiology: Inevitable or avoidable?","volume":"8","author":"Brady","year":"2017","journal-title":"Insights Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.jacr.2015.11.003","article-title":"Burnout of radiologists: Frequency, risk factors, and remedies: A report of the ACR Commission on Human Resources","volume":"13","author":"Harolds","year":"2016","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: Past, present and future","volume":"21","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.1016\/j.jacc.2018.03.521","article-title":"Artificial intelligence in cardiology","volume":"71","author":"Johnson","year":"2018","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1001\/jama.2013.393","article-title":"The inevitable application of big data to health care","volume":"309","author":"Murdoch","year":"2013","journal-title":"JAMA"},{"key":"ref_8","unstructured":"(2017, June 01). Administration UFaD. Guidance for Industry: Electronic Source Data in Clinical Investigations, Available online: https:\/\/www.fda.gov\/downloads\/drugs\/guidances\/ucm328691.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","article-title":"The practical implementation of artificial intelligence technologies in medicine","volume":"25","author":"He","year":"2019","journal-title":"Nat. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.carj.2018.02.002","article-title":"Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology","volume":"69","author":"Tang","year":"2018","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Coppola, F., Giannini, V., Gabelloni, M., Panic, J., Defeudis, A., Monaco, S.L., Cattabriga, A., Cocozza, M., Pastore, L., and Polici, M. (2021). Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice. Diagnostics, 11.","DOI":"10.3390\/diagnostics11050756"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1007\/s11547-021-01389-x","article-title":"A deep look into radiomics","volume":"126","author":"Scapicchio","year":"2021","journal-title":"Radiol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.3389\/fpsyg.2021.710982","article-title":"Human, All Too Human? An All-Around Appraisal of the \u201cArtificial Intelligence Revolution\u201d in Medical Imaging","volume":"12","author":"Coppola","year":"2021","journal-title":"Front. Psychol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1613\/jair.1.11222","article-title":"When will AI exceed human performance Evidence from AI experts","volume":"62","author":"Grace","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_18","unstructured":"(2021, September 24). Mura Dataset. Available online: https:\/\/stanfordmlgroup.github.io\/competitions\/mura\/."},{"key":"ref_19","unstructured":"Rajpurkar, P., Irvin, J., Bagul, A., Ding, D., Duan, T., Mehta, H., Yang, B., Zhu, K., Laird, D., and Ball, R.L. (2017). Mura: Large dataset for abnormality detection in musculoskeletal radiographs. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chada, G. (2019). Machine learning models for abnormality detection in musculoskeletal radiographs. Reports, 2.","DOI":"10.3390\/reports2040026"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1038\/s42256-019-0126-0","article-title":"Automated abnormality detection in lower extremity radiographs using deep learning","volume":"1","author":"Varma","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mondol, T.C., Iqbal, H., and Hashem, M. (2019, January 26\u201328). Deep CNN-based ensemble CADx model for musculoskeletal abnormality detection from radiographs. Proceedings of the  2019 5th International Conference on Advances in Electrical Engineering, Dhaka, Bangladesh.","DOI":"10.1109\/ICAEE48663.2019.8975455"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Teeyapan, K. (2020, January 3\u20134). Abnormality Detection in Musculoskeletal Radiographs using EfficientNets. Proceedings of the of the 24th International Computer Science and Engineering Conference, Bangkok, Thailand.","DOI":"10.1109\/ICSEC51790.2020.9375275"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"76649","DOI":"10.1109\/ACCESS.2021.3081915","article-title":"A Hybrid Two-Stage GNG\u2013Modified VGG Method for Bone X-rays Classification and Abnormality Detection","volume":"9","author":"Tantawi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","first-page":"1","article-title":"A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs","volume":"11","author":"He","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aziz, A.Z.B., Hasan, M., Mehedi, A., and Shin, J. (2021). Deep Transfer Learning-Based Musculoskeletal Abnormality Detection 2021. Proceedings of International Joint Conference on Advances in Computational Intelligence, Springer.","DOI":"10.1007\/978-981-16-0586-4_16"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kumar, Y., Kaur, K., and Singh, G. (2020, January 9\u201310). Machine learning aspects and its applications towards different research areas. Proceedings of the 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates.","DOI":"10.1109\/ICCAKM46823.2020.9051502"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Singh, G., and Anand, D. (2021, January 17\u201318). CompDNet-512: Hybrid Deep Learning Architecture for Prediction of COVID-19. Proceedings of the 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India.","DOI":"10.1109\/ICAC3N53548.2021.9725771"},{"key":"ref_29","unstructured":"Salgotra, R., Singh, S., Singh, U., Saha, S., and Gandomi, A.H. (2021, September 24). COVID-19: Time series datasets India versus world, Mendeley Data, Available online: https:\/\/data.mendeley.com\/datasets\/tmrs92j7pv\/23."},{"key":"ref_30","unstructured":"(2021, September 24). Python. Available online: https:\/\/www.python.org\/downloads\/release\/python-369\/."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ferroukhi, M., Ouahabi, A., Attari, M., Habchi, Y., and Taleb-Ahmed, A. (2019). Medical video coding based on 2nd-generation wavelets: Performance evaluation. Electronics, 8.","DOI":"10.3390\/electronics8010088"},{"key":"ref_32","first-page":"108","article-title":"Design and Implementation of Testing Tool for Code Smell Rectification Using C-Mean Algorithm","volume":"4","author":"Gurpreet","year":"2013","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"ref_33","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_34","unstructured":"Baldi, P., and Sadowski, P.J. (2013, January 5\u201310). Understanding dropout. Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_35","first-page":"12","article-title":"A novel digital signature algorithm based on biometric hash","volume":"9","author":"Shivangi","year":"2017","journal-title":"Int. J. Comput. Netw. Inf. Secur."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Anand, D., and Khemchandani, V. (2020). Data Security and Privacy Functions in Fog Computing for Healthcare 4.0. Fog Data Analytics for IoT Applications, Springer.","DOI":"10.1007\/978-981-15-6044-6_16"},{"key":"ref_37","unstructured":"Kaur, S.S., and Darpan, A. (2021, January 24\u201325). Smart Health Monitoring During Pandemic using Internet of Things. Proceedings of the 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India."},{"key":"ref_38","unstructured":"Kaur, S.S., and Darpan, A. (2021, January 2\u20134). Emotion Classification and Facial Key point detection using AI. Proceedings of the 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), Ernakulam, India."},{"key":"ref_39","unstructured":"Darpan, A., and Aashish, K. (2022). IoT-Based Automated Healthcare System. Advanced Healthcare Systems: Empowering Physicians with IoT-Enabled Technologies, John Wiley and Sons."}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/11\/5\/665\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:01:24Z","timestamp":1760137284000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/11\/5\/665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,26]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["biology11050665"],"URL":"https:\/\/doi.org\/10.3390\/biology11050665","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,26]]}}}