Hybrid Model using Feature Extraction and Non-linear SVM for Brain Tumor Classification
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It is essential to classify brain tumors from magnetic resonance imaging (MRI) accurately for better and timely treatment of the patients. In this paper, we propose a hybrid model, using VGG along with Nonlinear-SVM (Soft and Hard) to classify the brain tumors: glioma and pituitary and tumorous and non-tumorous. The VGG-SVM model is trained for two different datasets of two classes; thus, we perform binary classification. The VGG models are trained via the PyTorch python library to obtain the highest testing accuracy of tumor classification. The method is threefold, in the first step, we normalize and resize the images, and the second step consists of feature extraction through variants of the VGG model. The third step classified brain tumors using non-linear SVM (soft and hard). We have obtained 98.18 dataset and 99.78 accuracies for non-linear SVM are 95.50 and 97.95 linear and RBF kernel and 98.38 indicate that the hybrid VGG-SVM model, especially VGG 19 with SVM, is able to outperform existing techniques and achieve high accuracy.
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