Hybridization Techniques To Detect Brain Tumor

Abstract

: Diagnosing brain tumor in the present era through digital techniques need serious attention as the number of patients is increasing in an awkward manner. Magnetic Resonance Imaging is a tool that is used for the detection of brain tumors. Deploying Machine learning models to detect the abnormality pattern of the brain on MRI scans is quite beneficial in this modern era. This paper deploys GLCM on MRI scans to extract 66 features. Then, Feature selection and classification are applied to the given data set. Classification on a given data set is done through K- Nearest Neighbor. The given article classifies scans, i.e., normal and abnormal brain images. In the given study, we have taken normal and abnormal samples from the MRI department, Nishtar Medical hospital, Multan under doctor supervision. The scans were T2 weighted and having 256*256 pixels. In order to classify brain images, first, it needs to pre-process by skull stripping technique then the proposed algorithm is followed. The algorithm involves feature extraction through GLCM and feature selection through ACO in order to reduce the dimensions for optimal features. Results have proved its efficiency level up-to 88% on testing data.