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IDENTIFICATION OF ACUTE APPENDICITIS USING EUCLIDEAN DISTANCE ON SONOGRAPHIC IMAGE

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Author(s): R. Balu, | T. Devi

Journal: International Journal of Innovative Technology and Creative Engineering
ISSN 2045-869X

Volume: 1;
Issue: 7;
Start page: 32;
Date: 2011;
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Keywords: Image Mining | Euclidean Distance | Data Mining | Appendicitis | Abdomen

ABSTRACT
Acute Abdomen is defined as a syndrome induced by a wide variety of pathological conditions that require emergent medical or more often surgical management. The cardinal presenting symptom is abdominal pain which has many underlying causes. Over the past 10 years, sonography has gained acceptance for examining patients with acute abdominal pain. Sonography is dynamic, noninvasive, rapid, inexpensive, and readily accessible. It is very tedious and time consuming to analyze the sonographic images manually. The authors propose a novel method for diagnosing acute appendicitis using Euclidean distance measures. This paper details the image mining system that automates the diagnosis of acute appendicitis with significant speed up, experimentation methods, real data used for testing and the result.
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