Brain Tumor Classification using Convolutional Neural Networks and VGG16 Model

Electronics and Communication

Brain tumor is a collection, or mass ,of abnormal cells in brain. Detection of these tumor cells becomes challenging. Here, the Convolutional neural network will be used to classify the types of brain tumor. Till date, researchers have designed lots of brilliant deep convolutional neural networks(D-CNNs). However, most of the existing deep convolutional neural networks are trained with large datasets. It is quite rare for small datasets to take advantage of deep convolutional neural networks because of overfitting during implementation of those models. In this paper, we propose a modified deep neural network and use this model to fit a small size dataset. In this study, we propose a deep learning method which is used to classify Brain tumor types. Our method is based on VGG16 architecture with CNN as the classifier. We test our method using VGG16 dataset to measure the precision, recall, F-score and accuracy to evaluate our proposed method performance. The goal of our work is to show that a proper modified very deep model pre-trained on ImageNet for image classification can be used to fit very small dataset without severe overfitting.

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