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Semantic brain tumour segmentation is a significant contribution to medical image processing. This research is helpful for doctors making diagnoses and determining the severity of lesions. In recent years, convolutional neural networks have sometimes been known as self-remarkable representations in computer vision tasks. In three-dimensional medical image processing, CNN with architecture and skip connections has become increasingly popular. However, they are inadequate at both acquiring nearby and far-off semantic knowledge. However, the transformer's use of a vision. This accomplishment was made possible by the global information modelling capabilities of the transformer. When tryingto perform challenging prediction tasks, such as segmenting 3D medical pictures, it is vital to consider local and global features. In this study, we propose a hypothesis that predicts pre-processing steps that enhance the image quality and significantly impact accuracy with any statistical image classification method. We used a better picture enhancement methodology to back up our idea. This method has three unique stages: a median filter to eliminate noise, a histogram equalisation technique to boost contrast, and a grayscale-to-RGB conversion. The clipping limit of contrast-limited Adaptive Histogram Equalisation (CLAHE)is calculated by the Sailfish Optimization Algorithm (SOA) to achieve optimal picture enhancement. This study classifies semantically segmenting 3D brain tumours as a sequence-to-sequence prediction problem. U-net is proposed in the article as a novel method for segmenting 3D medical images; it incorporates a structure. A new technique for segmenting 3D medical images, U-net, is emerging. We trained and assessed our method on the BRATS 2019 dataset, which was found to outperform state-of-the-art algorithms and be competitive under similar settings. |
