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

Authors
  • Varçın, Fatih1
  • Erbay, Hasan2
  • Çetin, Eyüp3
  • Çetin, İhsan4
  • Kültür, Turgut5
  • 1 Department of Computer Engineering, Faculty of Engineering, Kırıkkale University, 71451, Kırıkkale, Turkey. [email protected] , (Turkey)
  • 2 Department of Computer Engineering, Faculty of Engineering, University of Turkish Aeronautical Association, 06790, Ankara, Turkey. , (Turkey)
  • 3 Department of Neurosurgery, Faculty of Medicine, Van Yüzüncü Yıl University, 65080, Van, Turkey. , (Turkey)
  • 4 Department of Medical Biochemistry, Faculty of Medicine, Hitit University, 19040, Corum, Turkey. , (Turkey)
  • 5 Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Kırıkkale University, 71450, Kırıkkale, Turkey. , (Turkey)
Type
Published Article
Journal
Journal of Digital Imaging
Publisher
Springer-Verlag
Publication Date
Feb 01, 2021
Volume
34
Issue
1
Pages
85–95
Identifiers
DOI: 10.1007/s10278-020-00402-5
PMID: 33432447
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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.

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