Brain Tumor Detection Using Hybrid Approach of Fish School Search Using SVM

The abnormal mass of the skull cell is called brain tumor accumulation. The brain tumor is mainly caused by a neuron in the skull by the uncontrollable increase in the additional nerve cell. The abnormalities in the cell division can lead to an aberrant cell proliferation that can threaten the operation of the human brain. The anomalous mass brain cell formed from brain aberrant mass can be identified as (benign) or cancerous (malignant) tumors from the normal tumor. Early detection and categorization of tumor kind of abnormal mass may be timely and can prevent additional brain damage. The latest hybrid strategy of a fish school search algorithm with Svm., which can segment easily the Mri brain image and define the optimal subset of cancerous cell features for reliable diagnosis of benign and malignant tumors is proposed in this study work. Fish School search algorithm is the nature-inspired algorithm based on the attitude of fish that can quickly segment the cancerous part or normal part from the soft tissue of the brain in the Mri image with less tuning parameter and rapid convergence. The hybrid FSSA -SVM approach includes phases like input Mri image data set, scanned Mri image preprocessing., elimination of noise from the picture using the medium filter, skull part stripping, fish school search algorithm based segmentation, and classification of tumor type via an SVM. According to the experimented result acquired from the performance of the Hybrid method of FSSA with Svm in identification, the tumor achieves the outperformance in terms of the accuracy as 98.43. This hybrid approach (FSSA- SVM) reduces the amount of time needed to evaluate and can quickly categories the tumor as benign or malignant.