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Deep Learning-based Pes Planus Classification Model Using Transfer Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(4), pp.21-28
  • DOI : 10.9708/jksci.2021.26.04.021
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : March 24, 2021
  • Accepted : April 12, 2021
  • Published : April 30, 2021

Kimyeonho 1 Namgyu Kim 1

1국민대학교

Accredited

ABSTRACT

This study proposes a deep learning-based flat foot classification methodology using transfer learning. We used a transfer learning with VGG16 pre-trained model and a data augmentation technique to generate a model with high predictive accuracy from a total of 176 image data consisting of 88 flat feet and 88 normal feet. To evaluate the performance of the proposed model, we performed an experiment comparing the prediction accuracy of the basic CNN-based model and the prediction model derived through the proposed methodology. In the case of the basic CNN model, the training accuracy was 77.27%, the validation accuracy was 61.36%, and the test accuracy was 59.09%. Meanwhile, in the case of our proposed model, the training accuracy was 94.32%, the validation accuracy was 86.36%, and the test accuracy was 84.09%, indicating that the accuracy of our model was significantly higher than that of the basic CNN model.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.