Abstract
Abstract: In recent years, Due to its powerful soft tissue comparison and non-invasive nature, magnetic resonance imaging,or MRI, has attracted a lot of attention recently. MRI is a frequently utilized imaging modality for the purpose of locating brain cancers in infants. The MRI generates a huge volume of data. Tumor heterogeneity, isointense and hypointense characteristics limit manual segmentation within a reasonable time frame, which in turn limits the application of trustworthy quantitative metrics in clinical practice. Manual segmentation tasks in clinical practice take a lot of time, and the operator's experience has a big impact on how well they execute. Tumor segmentation methods must be accurate and automated, however due to the extreme spatial and structural variety of brain tumors, this is a challenging task. We have suggested completely automatic brain categorization in our project. Using the DenseNet model, we have presented a fully automatic way to classify brain cancers. Tests conducted on the datasets demonstrate the effectiveness of our suggested approach for identifying baby brain cancers from MRI (MRI).
Published Version
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