{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:11:19Z","timestamp":1781280679724,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to other state-of-the-art deep CNN models. The innovation behind this research is that it works with an improved canny edge algorithm to obtain edges in the images that localize the fracture region. After that, grey images and their corresponding canny edge images are fed to the proposed hybrid SFNet for training and evaluation. Furthermore, the performance is also compared with the state-of-the-art deep CNN models on a bone image dataset. Our results showed that SFNet with canny (SFNet + canny) achieved the highest accuracy, F1-score and recall of 99.12%, 99% and 100%, respectively, for bone fracture diagnosis. It showed that using a canny edge algorithm improves the performance of CNN.<\/jats:p>","DOI":"10.3390\/s22155823","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T02:12:39Z","timestamp":1659665559000},"page":"5823","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML\/DL"],"prefix":"10.3390","volume":"22","author":[{"given":"Dhirendra Prasad","family":"Yadav","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashish","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8344-7611","authenticated-orcid":false,"given":"Senthil","family":"Athithan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4952-9282","authenticated-orcid":false,"given":"Abhishek","family":"Bhola","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3400-3504","authenticated-orcid":false,"given":"Bhisham","family":"Sharma","sequence":"additional","affiliation":[{"name":"Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh 174103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3339-0845","authenticated-orcid":false,"given":"Imed Ben","family":"Dhaou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hekma School of Engineering, Computing and Informatics, Dar Al-Hekma University, Jeddah 22246-4872, Saudi Arabia"},{"name":"Department of Computing, University of Turku, 20500 Turku, Finland"},{"name":"Higher Institute of Computer Sciences and Mathematics, Department of Technology, University of Monastir, Monastir 5000, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117421","DOI":"10.1016\/j.eswa.2022.117421","article-title":"Development of a GAN architecture based on integrating global and local information for paired and unpaired medical image translation","volume":"203","author":"Amirkolaee","year":"2022","journal-title":"Expert Syst. 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