Efficient Brain Tumor Segmentation with Multiscale Two-Pathway-Group Conventional Neural Networks

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Aim:

To segmented the Brain Tumor using Two-Pathway-Group Conventional Neural Networks.

Existing System:

Manual segmentation of brain tumors from large MRI images is a difficult and time-consuming task. Generative models require prior information and segmentation of brain tumors, whereas discriminative models depend on a set of features and classifiers. The most commonly adopted classifiers are support vector machines (SVM), random forests, neural networks and genetic algorithms. In contrast, automatic brain tumor segmentation methods use hand-designed features and a variety of image features intensity and texture.

Proposed System:

Two-Pathway-Group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the Two-Pathway CNN model to reduce instabilities and over fitting parameter sharing. Finally, we embed the cascade architecture into Two-Pathway- Group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive.