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Computational Analysis and Deep Learning for Medical Care


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1 Input Layer - - (227,227,3) 0 0 - relu - 2 CONV1 11 × 11 4 (55,55,96) 34,848 96 34,944 relu 105,415,200 3 POOL1 3 × 3 2 (27,27,96) 0 0 0 relu - 4 CONV2 5 × 5 1 (27,27,256) 614,400 256 614,656 relu 111,974,400 5 POOL2 3 × 3 2 (13,13,256) 0 0 0 relu - 6 CONV3 3 × 3 1 (13,13,384) 884,736 384 885,120 relu 149,520,384 7 CONV4 3 × 3 1 (13,13,384) 1,327,104 384 1,327,488 relu 112,140,288 8 CONV5 3 × 3 1 (13,13,256) 884,736 256 884,992 relu 74,760,192 9 POOL3 3 × 3 2 (6,6,256) 0 0 0 relu - 10 FC - - 9,216 37,748,736 4,096 37,752,832 relu 37,748,736 11 FC - - 4,096 16,777,216 4,096 16,781,312 relu 16,777,216 12 FC - - 4,096 4,096,000 1,000 4,097,000 relu 4,096,000 OUTPUT FC - - 1,000 - - 0 softmax - - - - - - - - 62,378,344 (Total) - -

      1.2.4 VGGNet