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Automatic detection and classification of brain tumours using k-means clustering with classifiers

Automatic detection and classification of brain tumours using k-means clustering with classifiers A brain tumour detection and classification system has been designed and developed. This work presents a new approach to the automated detection and classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumours based on k-means clustering and texture features, which separate brain tumour from healthy tissues in magnetic resonance images. The magnetic resonance feature image used for the tumour detection consists of T2-weighted magnetic resonance images for each axial slice through the head. The application of the proposed method for tracking tumour is demonstrated to help pathologists distinguish exactly tumour region and its type of tumour. The results are quantitatively evaluated by a human expert. The average overlap metric, average precision and the average recall between the results obtained using the proposed approach and ground truth are 0.92, 0.97 and 0.92, respectively. A classification with accuracy of 100%, 99% and 98% has been obtained by SVM, ANN and decision tree. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Enterprise Network Management Inderscience Publishers

Automatic detection and classification of brain tumours using k-means clustering with classifiers

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-1252
eISSN
1748-1260
DOI
10.1504/IJENM.2019.098103
Publisher site
See Article on Publisher Site

Abstract

A brain tumour detection and classification system has been designed and developed. This work presents a new approach to the automated detection and classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumours based on k-means clustering and texture features, which separate brain tumour from healthy tissues in magnetic resonance images. The magnetic resonance feature image used for the tumour detection consists of T2-weighted magnetic resonance images for each axial slice through the head. The application of the proposed method for tracking tumour is demonstrated to help pathologists distinguish exactly tumour region and its type of tumour. The results are quantitatively evaluated by a human expert. The average overlap metric, average precision and the average recall between the results obtained using the proposed approach and ground truth are 0.92, 0.97 and 0.92, respectively. A classification with accuracy of 100%, 99% and 98% has been obtained by SVM, ANN and decision tree.

Journal

International Journal of Enterprise Network ManagementInderscience Publishers

Published: Jan 1, 2019

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