Abstract
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.
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