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Bio or medical Information Technology (BIT)

Comparison of Performance According to Preprocessing Methods in Estimating %IMF of Hanwoo Using CNN in Ultrasound Images

초록

영어

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.

목차

Abstract
1. Introduction
2. The Previous CNN Method
3. Preprocessing Methods
3.1. Normalization
3.2. Histogram Equalization
3.3. Edge Enhancement
4. Results and Discussion
5. Conclusion
Acknowledgement
References

저자정보

  • Sang Hyun, Kim Professor, Department of Cyber Security, Youngsan University, Yangsan Campus, 288 Junam-ro, Yangsan, Gyeongnam, 50510, Korea

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