Abstract: Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. The use of small kernels allows designing a deeper architecture, besides having a positive effect against over fitting, given the fewer number of weights in the network. To investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. In addition to this tumor length, width and exact location is detected accurately. An extensive experimental evaluation on benchmark data demonstrates the effectiveness and efficiency of our approach.

Keywords: Image Segmentation, Brain tumors, Magnetic Resonance Imaging, Convolutional Neural Networks, Tumor segmentation etc.

PDF | DOI: 10.17148/IJARCCE.2021.10552

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