Huang Mei-Ling, Lin Ting-Yu
Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung, Taiwan.
Data Brief. 2020 Jun 25;31:105928. doi: 10.1016/j.dib.2020.105928. eCollection 2020 Aug.
Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification region. One of the drawbacks in breast mammography is breast cancer masses are more difficult to be found in extremely dense breast tissue. We select 106 breast mammography images with masses from INbreast database. Through data augmentation, the number of breast mammography images was increased to 7632. We utilize data augmentation on breast mammography images, and then apply the Convolutional Neural Networks (CNN) models including AlexNet, DenseNet, and ShuffleNet to classify these breast mammography images.
在众多癌症中，乳腺癌是女性死亡的第二大常见原因。早期发现和早期治疗可降低乳腺癌死亡率。乳房X光检查在乳腺癌筛查中发挥着重要作用，因为它可以检测早期乳腺肿块或钙化区域。乳房X光检查的一个缺点是在极度致密的乳腺组织中更难发现乳腺癌肿块。我们从INbreast数据库中选择了106张有肿块的乳房X光图像。通过数据增强，乳房X光图像的数量增加到了7632张。我们对乳房X光图像进行数据增强，然后应用包括AlexNet、DenseNet和ShuffleNet在内的卷积神经网络（CNN）模型对这些乳房X光图像进行分类。
