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A Fuzzy Decision Support System for Diagnosis of Some Liver Diseases in Educational Medical Institutions
International Journal of Fuzzy Logic and Intelligent Systems 2020;20(4):358-368
Published online December 25, 2020
© 2020 Korean Institute of Intelligent Systems.

Ahmed Abd El-badie Abd Allah Kamel and Faten Abd El-Sattar Zahran El-Mougi

Department of Computer Science, Faculty of Specific Education, Mansoura University, Egypt
Correspondence to: Ahmed Abd El-badie Abd Allah Kamel (ahmed_abdelbadie@mans.edu.eg)
Received August 14, 2020; Revised December 15, 2020; Accepted December 17, 2020.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
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 processingbased 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 secondorder 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.
Keywords : Decision support system, Fuzzy logic, Mamdani inference, Image processing, Liver diseases, Liver cancer