Brain tumor classification using sparse coding and dictionary learning

Brain tumor classification is considered as one of the most challenging tasks in medical imaging. In this paper, a novel approach for multi-class brain tumor classification based on sparse coding and dictionary learning is proposed. We propose an individual (per-class) dictionary learning and sparse coding classification using K-SVD algorithm. This approach combines topological and texture features to build and learn a dictionary. Experimental results demonstrate that the sparse coding based classification outperforms other state-of-the-art methods.