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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
Citations
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Statistical Analysis of GLCM Texture Features and Microstructures in SEM Images of Crassostrea virginica Exposed to Atrazine
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TL;DR: In this article, the effects of atrazine on oyster shells using scanning electron microscopy (SEM) images were taken of juvenile oysters to determine if there is a statistical significance in microstructure frequencies and texture features of three regions of the shell.
References
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