A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left">Models</td><td class="align_center">Dice index</td><td class="align_center">IOU</td><td class="align_center">Pixel accuracy</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">DenseNet</td><td class="align_center">0.7355</td><td class="align_center">0.5948</td><td class="align_center">0.7868</td></tr><tr><td class="align_left">Proposed model</td><td class="align_center">0.7697</td><td class="align_center">0.6041</td><td class="align_center">0.7983</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Quantitative comparisons using the proposed model.</div>

Computational Intelligence and Neuroscience

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Table 3

Table 3: A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer 