International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Comparison of Performance According to Preprocessing Methods in Estimating %IMF of Hanwoo Using CNN in Ultrasound Images

  • Kim, Sang Hyun (Department of Cyber Security, Youngsan University, Yangsan Campus)
  • Received : 2022.05.19
  • Accepted : 2022.05.25
  • Published : 2022.06.30
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Abstract

There have been various studies in Korea to develop a %IMF(Intramuscular Fat Percentage) estimation method suitable for Hanwoo. Recently, a %IMF estimation method using a convolutional neural network (CNN), a kind of deep learning method among artificial intelligence methods, has been studied. In this study, we performed a performance comparison when various preprocessing methods were applied to the %IMF estimation of ultrasound images using CNN as mentioned above. The preprocessing methods used in this study are normalization, histogram equalization, edge enhancement, and a method combining normalization and edge enhancement. When estimating the %IMF of Hanwoo by the conventional method that did not apply preprocessing in the experiment, the accuracy was 98.2%. The other hand, we found that the accuracy improved to 99.5% when using preprocessing with histogram equalization alone or combined regularization and edge enhancement.

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Acknowledgement

This work was supported by Youngsan University Research Fund of 2021.

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