抽象的な

Fusion of local and global features: A proficient advent for brain tumor classification with PSO and ELM

Josephine Sutha V, Latha P

Medical diagnostics is an important task to think about the inner structures and functions of human body and can be done through the investigation of digital imaging system. At the outset, detection of brain tumors is somewhat difficult in the diagnostic procedure. Magnetic Resonance Imaging (MRI) technique is a vibrant imaging method to locate the tissue contrast for anatomical details and to investigate the brain tumor locations. Contrast medium is the one frequently given before going for MRI or CT scans and is a matter that is absorbed or added into the veins to illuminate tumors. Consequently, at occasional times the image produced by the MR imaging test tends to go for illumination changes which shrinks the accuracy of automatic brain tumor detection scheme. Henceforth, in this work, GSIFT (Geometric Scale Invariant Terrain Feature Transform) is introduced as it has major progressions under blur and illumination fluctuations. Feature extraction of the proposed work is carried out by computing local features (GSIFT) and global features. Besides modified Particle Swarm Optimization (PSO) selection algorithm is applied for dimensionality reduction with the Extreme Learning Machine (ELM) Classification algorithm. The comparison ELM classifier assessment with and without PSO algorithms proves that the classification accuracy is higher in classification with PSO than the other. The proposed work is intended for automatic brain tumor detection and classification.