Change search
ReferencesLink to record
Permanent link

Direct link
Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine
Al-Madina Higher Institute for Engineering and Technology.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Minia University, Egypt.
Minia University, Egypt.
Number of Authors: 4
2016 (English)In: Building Sustainable Health Ecosystems: 6th International Conference on Well-Being in the Information Society, WIS 2016, Tampere, Finland, September 16-18, 2016, Proceedings / [ed] Hongxiu Li, Pirkko Nykänen, Reima Suomi, Nilmini Wickramasinghe, Gunilla Widén, Ming Zhan, Springer International Publishing , 2016, 151-160 p.Conference paper (Refereed)
Abstract [en]

The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.

Place, publisher, year, edition, pages
Springer International Publishing , 2016. 151-160 p.
Series
, Communications in Computer and Information Science, ISSN 1865-0929 ; 636
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
URN: urn:nbn:se:ltu:diva-38233DOI: 10.1007/978-3-319-44672-1_13ScopusID: 2-s2.0-84988509430Local ID: c8e9718c-d19f-477d-b44a-07703b7e110fISBN: 978-3-319-44671-4ISBN: 978-3-319-44672-1 (PDF)OAI: oai:DiVA.org:ltu-38233DiVA: diva2:1011732
Conference
6th International Conference on Well-Being in the Information Society, WIS 2016, Tampere, Finland, September 16-18
Note

För godkännande; 2016; Nivå 1; 20160904 (ismawa)

Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2016-10-27Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopushttp://link.springer.com/chapter/10.1007/978-3-319-44672-1_13

Search in DiVA

By author/editor
Awad, Ali Ismail
By organisation
Computer Science
Information Systems, Social aspects

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 50 hits
ReferencesLink to record
Permanent link

Direct link