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GLCM Textural Features for Brain Tumor Classification
N. S. Zulpe,Vrushsen Pawar +1 more
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TLDR
In this research work, four different classes of brain tumors are used and the GLCM based textural features of each class are extracted and applied to twolayered Feed forward Neural Network, which gives 97.5% classification rate.Abstract:
Automatic recognition system for medical images is challenging task in the field of medical image processing. Medical images acquired from different modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc which are used for the diagnosis purpose. In the medical field, brain tumor classification is very important phase for the further treatment. Human interpretation of large number of MRI slices (Normal or Abnormal) may leads to misclassification hence there is need of such a automated recognition system, which can classify the type of the brain tumor. In this research work, we used four different classes of brain tumors and extracted the GLCM based textural features of each class, and applied to twolayered Feed forward Neural Network, which gives 97.5% classification rate.read more
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Proceedings ArticleDOI
Un-Supervised MRI Segmentation Using Self Organised Maps
TL;DR: An unsupervised method for MR image segmentation based on Self Organizing Maps (SOMs) is presented, which is performed over real MR data provided by Internet Brain Repository (IBSR 2.0) database.
Journal ArticleDOI
Brain Tumor Classification Based on Singular Value Decomposition
TL;DR: A new method for detection of brain tumor based on singular value decomposition (SVD) is suggested, which is capable to classify the brain MR images into healthy and non-healthy image (that have a tumor).
Proceedings ArticleDOI
Classification of Brain Tumor using PCA-RF in MR Neurological Images
Vishlavath Saraswathi,Deep Gupta +1 more
TL;DR: This paper presents a multi-class brain tumor classification in MR neurological images using random forest (RF) classifier with three different approaches, showing that RF-PCA with random selection performs better than the other approaches.
Proceedings ArticleDOI
An efficient algorithm for textural feature extraction and detection of tumors for a class of brain MR imaging applications
TL;DR: An efficient textural feature extraction algorithm (TFEA) based on higher order statistical cumulant namely Kurtosis for a class of brain MR imaging applications obviating the need for employing specialized feature selection/reduction algorithms.
References
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Journal ArticleDOI
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