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The goal of this work concentrates on MRI (Magnetic Resonance Imaging) brain tumor findings by utilizing the BRATS dataset images. At first, it undergoes preprocessing criteria using an enhanced DWT (Discrete Wavelet Transform) filtering to decrease the noise level in the picture. Following this, segmentation has to extricate the tumor region dependent on improved thresholding activity. At that point, its effectiveness has processed by using the performance measurements, for example, Normalized cross-correlation, PSNR (Peak Signal to Noise Ratio), NAE (Normalized Absolute Error), and AD (Average Difference). The reproduction results show that unrivaled outcomes on the proposed thresholding task contrasted with comparable techniques. Furthermore, this was better than the other strategies for both Rician and Gaussian noise. The proposed approach supports the experts in identifying the exact location of the tumor and broadening their lifetime. [ABSTRACT FROM AUTHOR]

This paper presents Magnetic Resonance Imaging (MRI) brain tumor detection utilizing Fuzzy C Means strategy with an upgraded noise filtering calculation. A novel technique is proposed to enhance the execution of cerebrum tumor discovery. A new calculation for noise filtering is adapted to extract the correct area of tumor, where execution is enhanced by upgrading the threshold task in wavelet filtering strategy as a preprocessing step. Trial results demonstrate that by utilizing proposed calculation, the filtering procedure gives better execution when contrasted with the current methods. The average value of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) for Gaussian noise is improved by 40% and 41.06% and for Rician noise, which is 13.73% and 25.39% higher than the state-of-art methods. After filtering, segmentation is done to point out the tumor region. For segmentation, Otsu and FCM methods are adapted here and a comparison is made between these two methods. Experimental results show that Jaccard and Dice coefficient of Fuzzy C Means (FCM) with enhanced filtering is increased by 3.6% and 1.3% compared to the methods available in the literature. [ABSTRACT FROM AUTHOR]