Round Randomized Learning Vector Quantization for Brain Tumor Imaging

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/cmmm/volume-2016/8603609/figures/8603609.fig.0012a.svgz" name="8603609.fig.0012a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Brain tumor data set</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/cmmm/volume-2016/8603609/figures/8603609.fig.0012b.svgz" name="8603609.fig.0012b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Segment-challenge data set</td></tr><tr class="fig-image" id="c"><td><object data="https://static.hindawi.com/articles/cmmm/volume-2016/8603609/figures/8603609.fig.0012c.svgz" name="8603609.fig.0012c" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(c) </b>Segment-test data set</td></tr><tr class="fig-image" id="d"><td><object data="https://static.hindawi.com/articles/cmmm/volume-2016/8603609/figures/8603609.fig.0012d.svgz" name="8603609.fig.0012d" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(d) </b>Image segmentation with class data set</td></tr></table>

<div>The graph shows split percentage evaluation method that selected training (70%) and testing (30%) as suitable split percentage to split data for each data set.</div>

Computational and Mathematical Methods in Medicine

fig12

Figure 12

Figure 12: Round Randomized Learning Vector Quantization for Brain Tumor Imaging 