Deep Neural Network with 12-Norm Unit for Brain Lesions Detection

摘要

Automated brain lesions detection is an important and very challenging clinical diagnostic task, because the lesions have different sizes, shapes, contrasts and locations. Deep Learning recently shown promising progresses in many application fields, which motivates us to apply this technology for such important problem. In this paper we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). In order to investigate the applicability, we applied our approach on several brain diseases including high and low grade glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic Resonance Images (MRI) have been applied as input for the analysis. We proposed a new operation unit which receives features from several projections of a subset units of the bottom layer and computes a normalized 12-norm for next layer. We evaluated the proposed approach on two different CNN architectures and number of popular benchmark datasets. The experimental results demonstrate the superior ability of the proposed approach.