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Efficient Osteoporosis Prediction Using A Pair of Ensemble Models

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(12), pp.45-52
  • DOI : 10.9708/jksci.2021.26.12.045
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 28, 2021
  • Accepted : December 2, 2021
  • Published : December 31, 2021

Se-Heon Choi 1 Dong-Hwan Hwang 2 Kim, Do Hyun 1 Bak So Hyeon ORD ID 1 Kim, yoon 1

1강원대학교
2지오비전

Accredited

ABSTRACT

In this paper, we propose a prediction model for osteopenia and osteoporosis based on a convolutional neural network(CNN) using computed tomography(CT) images. In a single CT image, CNN had a limitation in utilizing important local features for diagnosis. So we propose a compound model which has two identical structures. As an input, two different texture images are used, which are converted from a single normalized CT image. The two networks train different information by using dissimilarity loss function. As a result, our model trains various features in a single CT image which includes important local features, then we ensemble them to improve the accuracy of predicting osteopenia and osteoporosis. In experiment results, our method shows an accuracy of 77.11% and the feature visualize of this model is confirmed by using Grad-CAM.

Citation status

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