Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/13438
Title: Implementation of medical decision support system for classification of brain tumor using image mining techniques
Researcher: Rajendran P
Guide(s): Madeshwaran, M.
Keywords: Medical decision support system, brain tumor, image mining techniques, computerized tomography, mining association rule in image database, ImageApriori algorithm
Upload Date: 28-Nov-2013
University: Anna University
Completed Date: 
Abstract: The development of Medical Decision Support System (MDSS) is becoming more important to support medical practitioners for better health care in the recent years. It has been witnessed that millions of medical images have been generated in medical care centers worldwide and are provided for medical diagnosis. Analysis of large data and manual interpretation become very challenging, even for the experienced and trained sonologist. Hence, physicians prefer computer based techniques for diagnosing the patients. This has initiated the research to create a vast database and implement various techniques for medical image analysis. The Computerized Tomography (CT) has been found to be the most reliable method for early detection of tumors. The present work consists of four phases: training phase, mining phase, test phase, and classification phase. In the training phase, acquired CT scan brain images are processed using the histogram equalization to improve the image quality. In the association rule mining phase, the association rules have been constructed from the stored feature vectors. This transaction representation is submitted to the proposed Mining Association Rule in Image database (MARI) and ImageApriori algorithm for association rule mining. In order to validate the obtained results, the algorithmic approach has been compared with the well known classifiers, a naive Bayesian classifier and associative classifier. The experimental results have shown that the proposed methods achieve high sensitivity (up to 97%), specificity (up to 91%) and accuracy (up to 98.5%). The average training and average testing time have been reduced to 4.39 sec and 1.01sec of CPU time respectively. Thus it is concluded that the brain images can be processed and tumor can be classified using image mining techniques for better decision making. newline newline newline
Pagination: xviii, 157
URI: http://hdl.handle.net/10603/13438
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File49.65 kBAdobe PDFView/Open
02_certificates.pdf1.22 MBAdobe PDFView/Open
03_abstract.pdf16.25 kBAdobe PDFView/Open
04_acknowledgement.pdf14.21 kBAdobe PDFView/Open
05_contents.pdf49.58 kBAdobe PDFView/Open
06_chapter 1.pdf108.84 kBAdobe PDFView/Open
07_chapter 2.pdf118.97 kBAdobe PDFView/Open
08_chapter 3.pdf414.5 kBAdobe PDFView/Open
09_chapter 4.pdf339.5 kBAdobe PDFView/Open
10_chapter 5.pdf127.93 kBAdobe PDFView/Open
11_chapter 6.pdf194.34 kBAdobe PDFView/Open
12_chapter 7.pdf20.21 kBAdobe PDFView/Open
13_references.pdf48.33 kBAdobe PDFView/Open
14_publications.pdf17.95 kBAdobe PDFView/Open
15_vitae.pdf11.39 kBAdobe PDFView/Open


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