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

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dc.authoridVarcin, Fatih / 0000-0002-5100-3012
dc.authorwosidVarcin, Fatih / ABE-7006-2020
dc.contributor.authorVarçın, Fatih
dc.contributor.authorErbay, Hasan
dc.contributor.authorÇetin, Eyüp
dc.contributor.authorÇetin, İhsan
dc.contributor.authorKültür, Turgut
dc.date.accessioned2021-11-01T15:06:03Z
dc.date.available2021-11-01T15:06:03Z
dc.date.issued2021
dc.department[Belirlenecek]
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.endpage95en_US
dc.identifier.issn0897-1889
dc.identifier.issn1618-727X
dc.identifier.issue1en_US
dc.identifier.pmid33432447
dc.identifier.scopus2-s2.0-85099399721
dc.identifier.scopusqualityQ1
dc.identifier.startpage85en_US
dc.identifier.urihttps://doi.org/10.1007/s10278-020-00402-5
dc.identifier.urihttps://hdl.handle.net/11491/7478
dc.identifier.volume34en_US
dc.identifier.wosWOS:000607060000003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorÇetin, İhsan
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/closedAccess
dc.subjectLumbar spondylolisthesisen_US
dc.subjectConvolutional neural networksen_US
dc.subjectYoloen_US
dc.subjectTransfer learningen_US
dc.titleEnd-To-End Computerized Diagnosis of Spondylolisthesis Using Only Lumbar X-rays
dc.typeArticle

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