An Efficient Approach to Detect Meningioma Brain Tumor Using Adaptive Neuro Fuzzy Inference System Method

Abstract

Detection of tumors in brain on time saves the patient life. The brain tumor detection is usually done in Magnetic Resonance Imaging (MRI) of the human brain. An automated model is framed to identify tumor pixels in method for detecting and image. This proposed method contains the following modules as enhancement, transformation, feature extraction, classifications and segmentation. The Oriented Local Histogram Equalization (OLHE) method is applied on the brain MRI images in order to enhance the pixel intensity in boundary regions. This enhanced brain image is transformed to multi orientation image using Gabor transform with respect to various scale and orientation of pixels. Then, set of features (Higher Order Spectra (HOS), Gradient, Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Curvelet) are extracted from this Gabor transformed image and these features are further trained and classified into benign or malignant using Adaptive Neuro Fuzzy Inference (ANFIS) classification approach. Finally, morphological algorithm is used for segmenting the tumor regions in the classified responses. MATLAB R2018 version is used in this paper to simulate the proposed algorithm for brain tumor detection. This proposed system achieves 98.6% of sensitivity, 99.5% of specificity and 99.4% of segmentation accuracy.