Abstract
Robust Classification of Primary Brain Tumor in MRI Images based on Multi Model Textures Features and Kernel Based SVM
Highlights
Medical image segmentation has core importance to implement high level operations such as tissues recognition and classification
The neural networks, we have utilized for comparative analysis is Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network
The performance analysis has been made by plotting the graphs of evaluation metrics such as sensitivity, specificity and the accuracy are shown in Table-1
Summary
Medical image segmentation has core importance to implement high level operations such as tissues recognition and classification. The threshold of the probability map is calculated to obtain the segmentation result or provide for postprocessing These techniques make it possible for high-dimensional features to be utilized in order to achieve a better discriminatory power for tumors compared with sole dependence on intensity information[10]. The approaches applied in the field of pattern analysis can be transplanted into medical image segmentation, such as a distance metric learning algorithm, to make the intra-class samples closer while keeping extraclass samples as far away from each other as possible These classifications based segmentation approaches consider the voxels in the image to be independent of each other, with no spatial correlation both in the training and testing phases[11]. A kernel function which defines an inner product in H performs the respective mapping leading to the following decision function f(x): The optimal hyperplane is the one with a maximal distance to the closest image φ ( Xi ) from the training data.The dual formulation can be stated as follows:
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