Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods

Maras, YukselTokdemir, GulUreten, KemalAtalar, EbruDuran, SemraMaras, Hakan2025-01-212025-01-2120222687-47842687-4792https://doi.org/10.52312/jdrs.2022.445https://search.trdizin.gov.tr/tr/yayin/detay522458https://hdl.handle.net/20.500.12587/24002Objectives: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. Materials and methods: In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease. Results: We compared the performances of the classification models in terms of performance metrics such as accuracy,eninfo:eu-repo/semantics/openAccessCervical radiography; convolutional neural network; deep; learning; disc space narrowing; osteoarthritic changes; transfer learningDiagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methodsArticle3319310110.52312/jdrs.2022.4452-s2.0-8512738364335361083Q2522458WOS:000778960800011Q3