Transfer Learning Using Convolutional Neural Network Architectures for Brain Tumor Classification from MRI Images

Chelghoum, Rayene; Ikhlef, Ameur; Hameurlaine, Amina; Jacquir, Sabir

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.