Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model

<table class="table-group" id="tab6"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Model</td><td class="align_center"><svg height="11.927pt" id="M54" style="vertical-align:-3.291101pt" version="1.1" viewbox="-0.0498162 -8.6359 18.3884 11.927" width="18.3884pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M686 28C612 35 607 44 591 112C563 234 541 360 519 489L489 666L457 658L147 121C100 40 89 36 24 28L17 0H240L250 28C168 34 159 41 190 101L262 237H482C495 180 503 137 510 91C517 47 514 35 441 28L433 0H677L686 28ZM475 280H285L429 541H431L475 280Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.244,3.132)"><path d="M387 400C387 425 348 451 303 451C247 451 176 414 132 376C69 322 24 228 24 148C24 43 74 -12 147 -12C211 -12 301 33 363 103L346 128C319 99 249 51 193 51C148 51 112 84 112 165C112 230 130 287 154 330C170 359 199 400 243 400C277 400 304 383 326 354C333 345 343 343 354 348C378 360 387 382 387 400Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,13.987,3.132)"><path d="M387 400C387 425 348 451 303 451C247 451 176 414 132 376C69 322 24 228 24 148C24 43 74 -12 147 -12C211 -12 301 33 363 103L346 128C319 99 249 51 193 51C148 51 112 84 112 165C112 230 130 287 154 330C170 359 199 400 243 400C277 400 304 383 326 354C333 345 343 343 354 348C378 360 387 382 387 400Z"></path></g></svg>%</td><td class="align_center"><span style="width: 26.6408ptpx;"><svg height="8.98583pt" id="M55" style="vertical-align:-0.2324905pt" version="1.1" viewbox="-0.0498162 -8.75334 26.6408 8.98583" width="26.6408pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M673 0V28C608 34 594 43 563 129C499 303 432 494 370 665L339 656L132 132C98 43 84 37 20 28V0H238V28C162 35 154 45 175 108C188 150 203 192 219 237H432C454 176 474 122 487 83S491 36 428 28V0H673ZM418 280H234C265 362 296 450 328 535H330L418 280Z"></path></g><g transform="matrix(.013,0,0,-0.013,8.226,0)"><path d="M716 650H478V622C549 617 570 605 575 566C579 535 582 493 582 401V326C582 161 537 40 393 40C269 40 205 133 205 294V521C205 611 213 617 291 622V650H33V622C111 615 120 611 120 521V272C120 154 148 80 208 34C252 0 309 -14 368 -14C434 -14 501 7 548 55C605 116 623 201 623 319V401C623 493 626 536 629 563C633 605 656 618 716 622V650Z"></path></g><g transform="matrix(.013,0,0,-0.013,17.794,0)"><path d="M614 175C564 76 510 21 408 21C256 21 146 149 146 336C146 488 235 629 402 629C510 629 570 586 597 480L626 488C620 541 614 582 606 638C578 643 510 665 429 665C206 665 44 527 44 316C44 157 153 -15 402 -15C474 -15 558 5 586 11C604 45 629 119 643 165L614 175Z"></path></g></svg></span></td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">ResNet34</td><td class="align_center">86.47</td><td class="align_center">0.9135</td></tr><tr><td class="align_left">SE-RseNet34</td><td class="align_center">87.36</td><td class="align_center">0.9097</td></tr><tr><td class="align_left">scSE-ResNet34</td><td class="align_center">83.96</td><td class="align_center">0.8722</td></tr><tr><td class="align_left">msSE-ResNet34-2way</td><td class="align_center">88.06</td><td class="align_center">0.9308</td></tr><tr><td class="align_left">msSE-ResNet34-3way</td><td class="align_center">88.87</td><td class="align_center">0.9541</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Comparison of categorization results between msSE-ResNet34 and other networks.</div>

Computational Intelligence and Neuroscience

tab6

Table 6

Table 6: Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model 