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Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine
Al-Madina Higher Institute for Engineering and Technology.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-3800-0757
Minia University, Egypt.
Minia University, Egypt.
Rekke forfattare: 42016 (engelsk)Inngår i: 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, s. 151-160Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer International Publishing , 2016. s. 151-160
Serie
Communications in Computer and Information Science, ISSN 1865-0929 ; 636
HSV kategori
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URN: urn:nbn:se:ltu:diva-38233DOI: 10.1007/978-3-319-44672-1_13ISI: 000392263900013Scopus ID: 2-s2.0-84988509430Lokal ID: c8e9718c-d19f-477d-b44a-07703b7e110fISBN: 978-3-319-44671-4 (tryckt)ISBN: 978-3-319-44672-1 (digital)OAI: oai:DiVA.org:ltu-38233DiVA, id: diva2:1011732
Konferanse
6th International Conference on Well-Being in the Information Society, WIS 2016, Tampere, Finland, September 16-18
Tilgjengelig fra: 2016-10-03 Laget: 2016-10-03 Sist oppdatert: 2018-07-10bibliografisk kontrollert

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Forlagets fulltekstScopushttp://link.springer.com/chapter/10.1007/978-3-319-44672-1_13

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