Exploring Deep Features from Brain Tumor Magnetic Resonance Images via Transfer Learning

Finding appropriate feature representations from radiological images is a vital task for prediction and diagnosis. Deep convolutional neural networks have recently achieved state-of-the-art performance in classification problems from several different domains. Research has also shown the feasibility of using a pre-trained deep neural network as a feature extractor when only a small dataset is available. This paper proposes a novel image feature extraction method for predicting survival time from brain tumor magnetic resonance images using pre-trained deep neural networks. Since all tumors are different sizes, we also explore different image resizing methods in the paper. We demonstrate that deep features can result in better survival time prediction with the highest accuracy of 95.45% versus conventional feature extraction methods from magnetic resonance images of the brain.