한국생산제조학회 학술지 영문 홈페이지

Current Issue

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 31 , No. 2

[ Papers ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 31, No. 2, pp. 94-100
Abbreviation: J. Korean Soc. Manuf. Technol. Eng.
ISSN: 2508-5107 (Online)
Print publication date 15 Apr 2022
Received 10 Mar 2022 Revised 28 Mar 2022 Accepted 29 Mar 2022
DOI: https://doi.org/10.7735/ksmte.2022.31.2.94

YOLOv5를 이용한 정상/비정상 적혈구 구별 알고리즘 개발
김진구a ; 강지훈b ; 최경철a ; 장웅기a, b ; 하호진a, b ; 임광석a, b ; 김병희a, b ; 박용재a, b, *

Development of a Normal/Abnormal RBC Detection Algorithm using YOLOv5
Jin-Gu Kima ; Ji-Hun Kangb ; Kyoung-Cheol Choia ; Woong-Ki Janga, b ; Ho-Jin Haa, b ; Kwang-Suk Lima, b ; Byeong-Hee Kima, b ; Yong-Jai Parka, b, *
aBiohealth-Machinery Convergence Engineering, Kangwon National University
bDepartment of Smart Health Science and Technology, Kangwon National University
Correspondence to : *Tel.: +82-33-250-6371 E-mail address: yjpark@kangwon.ac.kr (Yong-Jai Park).

Funding Information ▼

Abstract

Blood with cells such as WBC, RBC, and platelets, is often used in health diagnoses using morphological analyses. Among them, RBC is used as an indicator for diagnosing diseases such as diabetes, anemia, and heart failure; however, their accuracy is poor due to subjective analyst interpretations since they change reversibly based on various environmental factors such as viscosity, osmotic pressure, and calcium in the blood. In this study, an algorithm that objectively analyzes the shape of RBC and measures the ratio using YOLOv5, an AI technology, was developed and used to distinguish them as normal or abnormal. Data was augmented using image processing technology due to low data, and the YOLOv5 model was applied after labeling, where it was classified into four classes. The developed algorithm was verified through precision and recall, and subsequently applied to the test data to distinguish normal/abnormal red blood cells to obtain a ratio.


Keywords: Diagnosis AI algorithm, YOLOv5, Red blood cell, Image processing

Acknowledgments

이 연구는 2020년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No. 한국연구재단에서 부여한 과제번호: 2020R1I1A3073575/NRF-2020R1F1A1072926).


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Jin-Gu Kim

M.Sc. candidate in the Department of Interdisciplinary Program in Biohealth-machinery convergence engineering, Kangwon National University. His research interest is Machine.

E-mail: jink4763@gmail.com

Ji-Hun Kang

M.Sc. candidate in the Department of Interdisciplinary Program in Biohealth-machinery convergence engineering, Kangwon National University. His research interest is Machine.

E-mail: jihunkang0426@kangwon.ac.kr

Kyoung-Cheol Choi

M.Sc. candidate in the Department of Interdisciplinary Program in Biohealth-machinery convergence engineering, Kangwon National University. His research interest is biological.

E-mail: crudc925@gmail.com

Woong-Ki Jang

Post-doctoral associate in the Department of Smart Health Science and Technology, Kangwon National University. His research interest is Micro/nano scale surface texturing technologies and digital twin.

E-mail: wkddndrl@kangwon.ac.kr

Ho-Jin Ha

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interest is Machine.

E-mail: hojinha@kangwon.ac.kr

Kwang-Suk Lim

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interest is Antibody-drug conjugate, immuno-cancer cell therapy / gene and drug delivery system

E-mail: kslim@kangwon.ac.kr

Byeong-Hee Kim

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interest is micro and nano system design and precision control of machine tools.

E-mail: kbh@kangwon.ac.kr

Yong-Jai Park

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interest is Soft Robotics, Novel Mechanism Design, Bio-inspired Robot and Robot Application

E-mail: yjpark@kangwon.ac.kr