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A framework for medical image classification using soft set

Lashari, Tahira Anwar and Ibrahim, Rosziati (2013) A framework for medical image classification using soft set. In: 4th International Conference on Electrical Engineering and Informatics (ICEEI 2013), 24-25 June 2013, Universiti Kebangsaan Malaysia.

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Abstract

Medical image classification is a significant research area that receives growing attention from both the research community and medicine industry. It addresses the problem of diagnosis, analysis and teaching purposes in medicine. For these several medical imaging modalities and applications based on data mining techniques have been proposed and developed. Thus, the primary objective of medical image classification is not only to achieve good accuracy but to understand which parts of anatomy are affected by the disease to help clinicians in early diagnosis of the pathology and in learning the progression of a disease. This furnishes motivation from the advancement in data mining techniques and particularly in soft set, to propose a classification algorithm based on the notions of soft set theory. As a result, a new framework for medical imaging classification consisting of six phases namely: data acquisition, data pre-processing, data partition, soft set classifier, data analysis and performance evolution is presented. It is expected that soft set classifier will provide better results in terms of sensitivity, specificity, running time and overall classifier accuracy.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: medical image classification; data mining; texture classification; neural network; soft set
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 28 Aug 2013 02:06
Last Modified: 28 Aug 2013 02:06
URI: http://eprints.uthm.edu.my/id/eprint/4097
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