Background: The determination of tumor extent is a major challenging task in brain tumor planning and quantitative evaluation. Magnetic Resonance Imaging (MRI) is one of the non-intellectual technique has emerged as a front- line diagnostic tool for a brain tumor with non-ionizing radiation.
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Objectives: In Brain tumors, Gliomas is the very basic tumor of the brain; they might be less aggressive or more aggressive in a patient with a life expectancy of not more than 2 years. Manual segmentation is time-consuming so we use a deep convolutional neural network to increase the performance is highly dependent on the operator's experience.
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Methods: This paper proposed a fully automatic segmentation of brain tumors using deep convolutional neural networks. Further, it uses high-grade gliomas brain images from BRATS 2016 database. The suggested work achieve brain tumor segmentation using tensor flow, in which the anaconda frameworks are used to execute high-level mathematical functions.
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Results: Hence, the research work segments brain tumors into four classes like edema, non-enhancing tumor, enhancing tumor and necrotic tumor. Brain tumor segmentation needs to separate healthy tissues from tumor regions such as advancing tumor, necrotic core, and surrounding edema. We have presented a process to segment 3D MRI image of a brain tumor into healthy and area where the tumor is present, including their separate sub-areas. We have applied an SVM based classification. Categorization is complete using a soft-margin SVM classifier.
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Conclusion: We are using deep convolutional neural networks for presenting the brain tumor segmentation. Outcomes of the BRATS 2016 online judgment method assure us to increase the performance, accuracy, and speed with our best model. The fuzzy c-mean algorithm provides better accuracy and train on the SVM based classifier. We can achieve the finest performance and accuracy by using the novel two-pathway architecture i.e. encoder and decoder as well as the modeling local label that depends on stacking two CNN's
Deep Convolutional Neural Networks for Brain Tumor Segmentation: Boosting Performance Using Deep Transfer Learning: Preliminary Results
