지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.20 No.4
- 발행연도
- 2020.12
- 수록면
- 358 - 368 (11page)
- DOI
- 10.5391/IJFIS.2020.20.4.358
이용수
초록· 키워드
오류제보하기
Decision support systems improve medical diagnosis and minimize diagnostic errors. Existing diagnostic systems are often complex and exhibit limited performance on liver diseases, particularly the liver cancer. This paper presents a fuzzy decision support system for helping students diagnose some human liver diseases in educational medical institutions. The proposed system aims to improve real medical diagnosis processes. The approach has three basic steps: 1) symptoms-based diagnosis, 2) liver function-based diagnosis, and 3) image processing-based diagnosis. The proposed system employs two artificial intelligence techniques: fuzzy logic and image processing. The first is used for diagnosing liver diseases based on the liver function tests, while the second is used for diagnosing liver diseases such as the liver cancer, hepatitis, liver cirrhosis, liver fibrosis, and fatty liver. The proposed system combines two methods: the Mamdani inference and simulation method used in the MATLAB17 fuzzy logic toolbox, and the gray level co-occurrence matrix, for extracting the features of the second-order statistical texture of images acquired using computed tomography, magnetic resonance imaging, or ultrasound, for various liver diseases. Our results reveal a very good agreement between expert-made and system-made diagnoses, suggesting high accuracy.
목차
Abstract
1. Introduction
2. Related Works
3. Proposed Approach
4. Application and Experimental Results
5. Conclusions and Future Work
References
참고문헌 (19)
참고문헌 신청
S. Subbulakshmi / 2018 / A fuzzy logic decision support system for the diagnosis of heart disease / IOSR Journal of Engineering 8(8):70~77
G. Improta / 2020 / Fuzzy logic–based clinical decision support system for the evaluation of renal function in posttransplant patients / Journal of Evaluation in Clinical Practice 26(4):1224~1234
M. R. Farokhzad / 2016 / A novel adaptive neuro fuzzy inference system for the diagnosis of liver disease / International Journal of Academic Research in Computer Engineering 1(1):61~66
M. Rajabi / Hybrid adaptive neuro-fuzzy inference system for diagnosing the liver disorders
G. Ahmad / 2019 / Automated diagnosis of hepatitis b using multilayer mamdani fuzzy inference system / Journal of Healthcare Engineering 2019
G. Improta / 2020 / Fuzzy logic–based clinical decision support system for the evaluation of renal function in posttransplant patients / Journal of Evaluation in Clinical Practice 26(4):1224~1234
M. R. Farokhzad / 2016 / A novel adaptive neuro fuzzy inference system for the diagnosis of liver disease / International Journal of Academic Research in Computer Engineering 1(1):61~66
M. Rajabi / Hybrid adaptive neuro-fuzzy inference system for diagnosing the liver disorders
G. Ahmad / 2019 / Automated diagnosis of hepatitis b using multilayer mamdani fuzzy inference system / Journal of Healthcare Engineering 2019
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