End-To-End Computerized Diagnosis of Spondylolisthesis Using Only Lumbar X-rays.
Lumbar 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 tre...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 1; pp. 85 - 96 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148753838&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148753838 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2021 vid: 34 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148753838 148058638 148753838 148753838 10.1007/s10278-020-00402-5 148753838 ppf: 85 ppct: 11 formats: fmt: @attributes: type: P tig: atl: End-To-End Computerized Diagnosis of Spondylolisthesis Using Only Lumbar X-rays. aug: au: Varçın, Fatih Erbay, Hasan Çetin, Eyüp Çetin, İhsan Kültür, Turgut affil: Department of Computer Engineering, Faculty of Engineering, Kırıkkale University, 71451, Kırıkkale, Turkey sug: subj: Spondylolisthesis Diagnosis Spondylolisthesis Radiography Digital Imaging Methods Lumbar Vertebrae Pathology Deep Learning Human Neural Networks (Computer) ROC Curve ab: Lumbar 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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