Brain Tumor Classification in MRI Scans Using Sparse Representation

Recent advancement in biomedical image processing using Magnetic Resonance Imaging (MRI) makes it possible to detect and localize brain tumors with ease. However, reliable classification of brain tumor types using MRI still remains a challenging problem. In this paper we propose a sparse representation based approach to successfully classify tumors in brain MRI. We aim to classify brain scans into eight (8) different categories with seven (7) indicating different tumor types and one for normal brain. This allows the proposed approach to not only classify brain tumors but also to detect their existence. The proposed classification approach is validated using Leave 2-Out cross-validation technique. The result obtained from the proposed approach is then compared with a recent technique presented in literature. The comparison clearly shows that the proposed approach outperforms the existing technique both in terms of accuracy and number of classes being employed.