A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

<div>The architecture of VGG16. It consists of 13 convolutional layers, 3 full-connection layers, 5 pooling layers, and 1 softmax layer in addition to the input and output layers.</div>

Computational and Mathematical Methods in Medicine

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Figure 2

Figure 2: A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis 