Adaptive neuro-FUZZY inference system-fusion-deep belief network for brain tumor detection using MRI images with feature extraction

The abnormalities arises withinh the brain cells results in brain tumors (BT) and this emerges as a life-threatening diseases that results in increased death rate day by day. However, accurate segmentation is required to detect BT because large spatial and structural variability between BT makes automatic segmentation difficult. In order to improve the detection rate, the proposed method named the Adaptive Neuro-fuzzy Inference System-Fusion-Deep Belief Network (ANFIS-F-DBN) model is developed in this research. At first, the brain image acquired form the specified dataset is pre-processed using the Gaussian filter and then, the extraction of Regions of interest (ROI) is done. Then, the Thresholding Transformation is used for the image enhancement process. After that, the Deep Fuzzy Clustering (DFC) is employed to segment the brain tumor area and the image augmentation process is done by different steps, like sharpening, translation, zooming and padding. In addition, the extraction of features namely, statistical features and Entropy with Local Directional Pattern Variance (LDPv) are done in the feature extraction phase. At last, the BT detection is executed based on hybrid deep learning (DL) namely, ANFIS-F-DBN. Moreover, the metrics like accuracy, sensitivity and specificity are used to analyze the performance of the devised scheme and obtained a value of 90.00%, 90.60% and 91.90%, respectively.