  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% accuracy for the first
dataset and 99.78% for the second dataset using VGG19. The classification
accuracies for non-linear SVM are 95.50% and 97.98% with linear and rbf kernel
and 97.95% for soft SVM with RBF kernel with D1, and 96.75% and 98.60% with
linear and RBF kernel and 98.38% for soft SVM with RBF kernel with D2. Results
indicate that the hybrid VGG-SVM model, especially VGG 19 with SVM, is able to
outperform existing techniques and achieve high accuracy.
