ANALYSIS ON DEEP LEARNING TO DETECT THE BRAIN MRI TUMOR SEGEMENTATION
Keywords:
Brain tumour, Local feature, Image classification, Segmentation, Residual.Abstract
In this recent scenario, brain tumour detection system through classification technique helps in the person infected with brain tumour and plays an important role to provide effectiveness in the diagnosis process and proper treatment. The above MRI classification method helps to provide effective and early-stage treatment for identifying the tumour in the brain and determine the level of tumour occurrences. As there are several classification methods related to brain tumour exists in recent days, which is associated with U-Net architecture i.e., deep learning methods related to medical classification. Based on the comparison of the feature information among low-level and high-level, in this paper the propose architecture of Residual U-NET with some enhanced local feature information as it helps to improve the medical image segmentation. Through this, propose work helps to highlight the improvement in the attention module for the segmentation of image tumour, propose the novel attention module based on the Residual U-Net model. In the modified Residual U-Net model, residual module and attention gate is addressed along with dropout and wide context layers. Here the addition of salient feature information, which helps to focus on the large sensitive scaled information but also consider the small-scale images also. In the performance analysis, modified model associated with gate attention outperforms with the existing models like, U-Net and CNN Densely models.
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