Brain tumor using CNN
In this project, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance image scans. The data from multi-modal brain tumor segmentation challenge are utilized which are coregistered and skull stripped, and the histogram matching is performed with a reference volume of high contrast. We are detecting tumor by using preprocessing, segmentation, feature extraction, optimization and lastly classification after those preprocessed images use to classify the tissue. We performed a leave-one out cross-validation and achieved 88 Dice overlap for the complete tumor region, 75 for the core tumor region and 95 for enhancing tumor region, which is higher than the Dice overlap reported.
Keywords: Machine Learning, CNN Algorithm, Deep Learning, Classification
System Architecture The system diagram depicts various steps according to various events that are performed in the proposed system.
The detection and diagnosis of brain tumor from MRI is crucial to decrease the rate of casualties. Brain tumor is difficult to cure, because the brain has a very complex structure and the tissues are interconnected with each other in a complicated manner. The Proposed system will help to detect different kinds of tumors from the MRI images with much more clarity and accuracy which will help bring down the number of casulaties. The detection of brain tumors through the proposed system will not only identify the affected part of the brain but also to the tumor shape, size, boundary, and position.
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