Prediction of Tumors in Brain MRI Images using Grasshopper Optimization Algorithm with Image Segmentation and Enhancement Approach

Abstract

A brain tumor is one of the most serious diseases that lead to death of the patient. The treatment of the patients having brain tumors greatly depend on early detection of the tumors. The brain tumor can be viewed comprehensively through MRI image. The analysis of brain tumor must be done in accurate way. To analyze the brain tumor, segmentation is a solution. In medical image processing, brain tumor segmentation is very significant. Thus, various approaches for image-segmentation have been proposed in the existing works for proper diagnosis of the brain tumor; however, it has been analyzed that the existing techniques does not lead to attain optimal results. Therefore, in the proposed work, Grasshopper optimization algorithm (GOA) is used for image segmentation. Also, in the proposed work, the image enhancement technique and filter is used to improve the visualization and quality of the image. The performance of the proposed GOA-based approach for detecting the brain tumor is analyzed and compared with different existing techniques in terms of various parameters and the obtained results demonstrated the efficiency of the proposed approach over existing approaches for brain tumor detection.