Classifications of meningioma brain images using the novel Convolutional Fuzzy C Means (CFCM) architecture and...

In this paper, meningioma detection and segmentation method is proposed. This research work proposes an effective method to locate meningioma pictures through a novel CFCM classification approach. This proposed method consist of Non-Sub sampled Contourlet Transform decomposition module which decomposes the entire brain image into multi-scale sub-band images and then the heuristic and uniqueness features have been computed individually. Then, these heuristic and uniqueness features are trained and classified using Convolutional Fuzzy C Means (CFCM) classifier. This proposed method is applied on two independent brain imaging datasets. The proposed meningioma identification system stated in this work obtained 98.81% of Se, 98.83% of Sp, 99.04% of Acc, 99.12% of pr, and 99.14% of FIS on Nanfang University dataset brain images. The proposed meningioma identification system stated in this work obtained 98.92% of Se, 98.88% of Sp, 98.9% of Acc, 98.88% of pr, and 99.36% of FIS on the BRATS 2021 brain images. Finally, the tumour segmentation module is designed in VLSI, and it is simulated using Xilinx project navigator in this paper.