End-To-End Computerized Diagnosis of Spondylolisthesis Using Only Lumbar X-rays

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dc.authoridVarcin, Fatih/0000-0002-5100-3012
dc.contributor.authorVarcin, Fatih
dc.contributor.authorErbay, Hasan
dc.contributor.authorCetin, Eyup
dc.contributor.authorCetin, Ihsan
dc.contributor.authorKultur, Turgut
dc.date.accessioned2025-01-21T16:38:33Z
dc.date.available2025-01-21T16:38:33Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description.abstractLumbar spondylolisthesis (LS) is the anterior shift of one of the lower vertebrae about the subjacent vertebrae. There are several symptoms to define LS, and these symptoms are not detected in the early stages of LS. This leads to disease progress further without being identified. Thus, advanced treatment mechanisms are required to implement for diagnosing LS, which is crucial in terms of early diagnosis, rehabilitation, and treatment planning. Herein, a transfer learning-based CNN model is developed that uses only lumbar X-rays. The model was trained with 1922 images, and 187 images were used for validation. Later, the model was tested with 598 images. During training, the model extracts the region of interests (ROIs) via Yolov3, and then the ROIs are split into training and validation sets. Later, the ROIs are fed into the fine-tuned MobileNet CNN to accomplish the training. However, during testing, the images enter the model, and then they are classified as spondylolisthesis or normal. The end-to-end transfer learning-based CNN model reached the test accuracy of 99%, whereas the test sensitivity was 98% and the test specificity 99%. The performance results are encouraging and state that the model can be used in outpatient clinics where any experts are not present.
dc.identifier.doi10.1007/s10278-020-00402-5
dc.identifier.endpage95
dc.identifier.issn0897-1889
dc.identifier.issn1618-727X
dc.identifier.issue1
dc.identifier.pmid33432447
dc.identifier.scopus2-s2.0-85099399721
dc.identifier.scopusqualityQ1
dc.identifier.startpage85
dc.identifier.urihttps://doi.org/10.1007/s10278-020-00402-5
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24689
dc.identifier.volume34
dc.identifier.wosWOS:000607060000003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Digital Imaging
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectLumbar spondylolisthesis; Convolutional neural networks; Yolo; Transfer learning
dc.titleEnd-To-End Computerized Diagnosis of Spondylolisthesis Using Only Lumbar X-rays
dc.typeArticle

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