Transfer Learning Using Convolutional Neural Network Architectures for Brain Tumor Classification from MRI Images
Résumé
Brain tumor classification is very important in medical applications to
develop an effective treatment. In this paper, we use brain contrast-enhanced magnetic
resonance images (CE-MRI) benchmark dataset to classify three types of brain tumor
(glioma, meningioma and pituitary). Due to the small number of training dataset, our
classification systems evaluate deep transfer learning for feature extraction using nine
deep pre-trained convolutional Neural Networks (CNNs) architectures. The objective
of this study is to increase the classification accuracy, speed the training time and
avoid the overfitting. In this work, we trained our architectures involved minimal pre-
processing for three different epoch number in order to study its impact on
classification performance and consuming time. In addition, the paper benefits
acceptable results with small number of epoch in limited time. Our interpretations
confirm that transfer learning provides reliable results in the case of small dataset. The
proposed system outperforms the state-of-the-art methods and achieve 98.71%
classification accuracy