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A Novel Intelligent System for Diagnosing some of Humans' Respiratory System Diseases

by A. E. E. Elalfi, M. E. E. Elalmi, F. A. Zahran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 41
Year of Publication: 2019
Authors: A. E. E. Elalfi, M. E. E. Elalmi, F. A. Zahran
10.5120/ijca2019918449

A. E. E. Elalfi, M. E. E. Elalmi, F. A. Zahran . A Novel Intelligent System for Diagnosing some of Humans' Respiratory System Diseases. International Journal of Computer Applications. 181, 41 ( Feb 2019), 19-29. DOI=10.5120/ijca2019918449

@article{ 10.5120/ijca2019918449,
author = { A. E. E. Elalfi, M. E. E. Elalmi, F. A. Zahran },
title = { A Novel Intelligent System for Diagnosing some of Humans' Respiratory System Diseases },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2019 },
volume = { 181 },
number = { 41 },
month = { Feb },
year = { 2019 },
issn = { 0975-8887 },
pages = { 19-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number41/30335-2019918449/ },
doi = { 10.5120/ijca2019918449 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:46.485940+05:30
%A A. E. E. Elalfi
%A M. E. E. Elalmi
%A F. A. Zahran
%T A Novel Intelligent System for Diagnosing some of Humans' Respiratory System Diseases
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 41
%P 19-29
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a novel intelligent system for diagnosing some of humans' respiratory diseases. The proposed system aims to simulate the real medical diagnosing ‎processes. It was adopted in diagnosis on two main parts; knowledge base (KB) and ‎image processing (IP). This paper combines two diagnostic methods; the decision tree (DT) method which used J48 algorithm in WEKA 3.7, and the gray level co-occurrence matrix (GLCM) method for extracting second order statistical texture features of chest x-ray images. The weighted ‎euclidean distance ‎‎ (WED) algorithm was used for feature matching. The final decision calculated by probability measure method for independent events, which is depending on "multiplication rule". The proposed system implemented via visual studio.net 2017; used for designing the main graphical user interface (GUI), MATLAB17; used for image processing diagnoses, and LabVIEW17; used for knowledge base diagnoses. The obtained results show that there is a ‎good agreement between expert's diagnoses and proposed system diagnoses with a high accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Knowledge base Image processing ‎Intelligent systems Human respiratory system Expert systems Lab VIEW

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