한국가시화정보학회지 (Journal of the Korean Society of Visualization)

  • 제19권1호
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  • Pages.57-61
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  • 2021
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  • 1598-8430(pISSN)
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  • 2093-808X(eISSN)
한국가시화정보학회 (The Korean Society of Visualization)
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Gated recurrent unit (GRU) 신경망을 이용한 적혈구 침강속도 예측

Forecasting of erythrocyte sedimentation rate using gated recurrent unit (GRU) neural network

  • Lee, Jaejin (School of Mechanical Engineering, PNU) ;
  • Hong, Hyeonji (Eco-friendly Smart Ship Parts Technology Innovation Center, PNU) ;
  • Song, Jae Min (Department of Oral and Maxilofacial Surgery) ;
  • Yeom, Eunseop (School of Mechanical Engineering, Pusan National University (PNU))
  • 투고 : 2021.03.09
  • 심사 : 2021.04.08
  • 발행 : 2021.04.30
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초록

In order to determine erythrocyte sedimentation rate (ESR) indicating acute phase inflammation, a Westergren method has been widely used because it is cheap and easy to be implemented. However, the Westergren method requires quite a long time for 1 hour. In this study, a gated recurrent unit (GRU) neural network was used to reduce measurement time of ESR evaluation. The sedimentation sequences of the erythrocytes were acquired by the camera and data processed through image processing were used as an input data into the neural network models. The performance of a proposed models was evaluated based on mean absolute error. The results show that GRU model provides best accurate prediction than others within 30 minutes.

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과제정보

이 논문은 2019년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No. NRF-2019R1F1A1062348).

참고문헌

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