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Application of wavelet de-noising filters in mammogram images classification using fuzzy soft set

Anwar Lashari, Saima and Ibrahim, Rosziati and Senan, Norhalina and Yanto, Iwan Tri Riyadi and Herawan, Tutut (2017) Application of wavelet de-noising filters in mammogram images classification using fuzzy soft set. In: Advances in Intelligent Systems and Computing. Recent Advances on Soft Computing and Data Mining, 549 . Springer International Publishing, pp. 529-537. ISBN 9783319512792

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Recent advances in the field of image processing have revealed that the level of noise in mammogram images highly affect the images quality and classification performance of the classifiers. Whilst, numerous data mining techniques have been developed to achieve high efficiency and effectiveness for computer aided diagnosis systems. However, fuzzy soft set theory has been merely experimented for medical images. Thus, this study proposed a classifier based on fuzzy soft set with embedding wavelet de-noising filters. Therefore, the proposed methodology involved five steps namely: MIAS dataset, wavelet de-noising filters hard and soft threshold, region of interest identification, feature extraction and classification. Therefore, the feasibility of fuzzy soft set for classification of mammograms images has been scrutinized. Experimental results show that proposed classifier FussCyier provides the classification performance with Daub3 (Level 1) with accuracy 75.64% (hard threshold), precision 46.11%, recall 84.67%, F-Micro 60%. Thus, the results provide an alternative technique to categorize mammogram images.

Item Type: Book Section
Uncontrolled Keywords: Mammogram images; feature extraction; wavelet filters; fuzzy soft set
Subjects: Q Science > QA Mathematics > QA150 Algebra
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:34
Last Modified: 13 Aug 2018 03:34
URI: http://eprints.uthm.edu.my/id/eprint/9600
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