Performance analysis of meningioma Brain tumor detection system using Soft computing approaches

Abstract

The development of abnormal cells in human brain leads to the formation of tumors. This research work proposes an efficient approach for meningioma brain tumor detection and segmentation using image fusion and Co-Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification method. The brain MRI images are fused and the Dual Tree Complex Wavelet Transform (DTCWT) is applied on the fused image. Then, the statistical features, Local Ternary Pattern (LTP) features and Grey Level Co-occurrence Matrix (GLCM) features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The meningioma tumors are also classified and segmented using soft computing methods in this research work. The noise contents are detected and reduced using directional filters and then Gabor transform is applied on this noise smoothed brain image for transforming the spatial pixels into multi resolution pixels. Further, features are derived from this Gabor transformed multi resolution image and these are optimized using ant feature learning optimization algorithm. These optimized features are classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classification approach and then morphological segmentation method is applied on this classified abnormal meningioma brain image in order to segment the tumor regions. The proposed meningioma tumor detection system obtains 98.1% of sensitivity, 99.75 of specificity, 99.6% of accuracy, 98.55 of precision, 97.95 of F1-Score and 98.1% of relevance factor. newline