Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images
With the advent of more powerful computing devices, system automation plays a pivotal role. In the medical industry, automated image classification and segmentation is an important task for decision making about a particular disease. In this research, a new technique is presented for classification...
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| Format: | Article |
| Language: | English |
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IEEE
2017
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| Online Access: | http://ir.unimas.my/id/eprint/16540/ http://ir.unimas.my/id/eprint/16540/1/multiclass%20brain%20glioma%20tumor%20classification%20%28abstract%29.pdf |
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| author | Ghazanfar, Latif Butt, M. Mohsin Khan, Adil H. Butt, Omair Awang Iskandar, D.N.F. |
| author_facet | Ghazanfar, Latif Butt, M. Mohsin Khan, Adil H. Butt, Omair Awang Iskandar, D.N.F. |
| author_sort | Ghazanfar, Latif |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | With the advent of more powerful computing devices, system automation plays a pivotal role. In the medical industry, automated image classification and segmentation is an important task for decision making about a particular disease. In this research, a new technique is presented for classification and segmentation of low-grade and high-grade glioma tumors in Multimodal Magnetic Resonance (MR) images. In the proposed system, each multimodal MR image is divided into small blocks and features of each block are extracted using three Dimensional Discrete Wavelet Transform (3D DWT). Random Forest classifier is used for the classification of multiple Glioma tumor classes, then segmentation is performed by reconstructing the MR image based on the classified blocks. MICCAI BraTS dataset is used for testing the proposed technique and experiments are performed for Low Grade Glioma (LGG) and High Grade Glioma (HGG) datasets. The results are compared with different classifiers e.g. multilayer perceptron, radial basis function, Naïve Bayes, etc., After careful analysis, Random Forest classifier provided better precision by securing average accuracy of 89.75% and 86.87% is obtained for HGG and LGG respectively. |
| first_indexed | 2025-11-15T06:49:55Z |
| format | Article |
| id | unimas-16540 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:49:55Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-165402017-10-13T01:34:58Z http://ir.unimas.my/id/eprint/16540/ Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images Ghazanfar, Latif Butt, M. Mohsin Khan, Adil H. Butt, Omair Awang Iskandar, D.N.F. T Technology (General) With the advent of more powerful computing devices, system automation plays a pivotal role. In the medical industry, automated image classification and segmentation is an important task for decision making about a particular disease. In this research, a new technique is presented for classification and segmentation of low-grade and high-grade glioma tumors in Multimodal Magnetic Resonance (MR) images. In the proposed system, each multimodal MR image is divided into small blocks and features of each block are extracted using three Dimensional Discrete Wavelet Transform (3D DWT). Random Forest classifier is used for the classification of multiple Glioma tumor classes, then segmentation is performed by reconstructing the MR image based on the classified blocks. MICCAI BraTS dataset is used for testing the proposed technique and experiments are performed for Low Grade Glioma (LGG) and High Grade Glioma (HGG) datasets. The results are compared with different classifiers e.g. multilayer perceptron, radial basis function, Naïve Bayes, etc., After careful analysis, Random Forest classifier provided better precision by securing average accuracy of 89.75% and 86.87% is obtained for HGG and LGG respectively. IEEE 2017 Article PeerReviewed text en http://ir.unimas.my/id/eprint/16540/1/multiclass%20brain%20glioma%20tumor%20classification%20%28abstract%29.pdf Ghazanfar, Latif and Butt, M. Mohsin and Khan, Adil H. and Butt, Omair and Awang Iskandar, D.N.F. (2017) Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images. 4th International Conference on Electrical and Electronic Engineering (ICEEE), 2017. pp. 333-337. ISSN ISBN: 978-150906788-6 http://ieeexplore.ieee.org/abstract/document/7935845/ DOI: 10.1109/ICEEE2.2017.7935845 |
| spellingShingle | T Technology (General) Ghazanfar, Latif Butt, M. Mohsin Khan, Adil H. Butt, Omair Awang Iskandar, D.N.F. Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images |
| title | Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images |
| title_full | Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images |
| title_fullStr | Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images |
| title_full_unstemmed | Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images |
| title_short | Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images |
| title_sort | multiclass brain glioma tumor classification using block-based 3d wavelet features of mr images |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/16540/ http://ir.unimas.my/id/eprint/16540/ http://ir.unimas.my/id/eprint/16540/ http://ir.unimas.my/id/eprint/16540/1/multiclass%20brain%20glioma%20tumor%20classification%20%28abstract%29.pdf |