Publication

Abstract : Brain tumor is a type of cancerous growth that may occur in the brain. Early diagnosis of the disease is crucial for proper treatment. Diagnosis of brain tumors is usually done using images obtained through magnetic resonance imaging (MRI). MRI images can be classified using a Convolutional Neural Network (CNN), which is a technique in deep learning. It is suitable for classifying large image datasets. Support Vector Machine (SVM) is a technique in machine learning that is predominantly used for classification and in various regression problems. In this paper, we classified brain MRI images using pre-trained models like AlexNet, VGG16, InceptionV3, and ResNet50. Finally, a CNN model and an SVM model are trained with the same dataset. Using the results thus obtained a hybrid CNN-SVM model has been built to get better accuracy and prediction results.