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首页> 外文期刊>Computational and mathematical methods in medicine >Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms
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Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms

机译：使用作弊表的卷积神经网络和数据增强来检测乳房X光检查的乳腺癌

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The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can reduce the risk of death from this disease. DL through CNN can assist imaging specialists in classifying the mammograms accurately. Accurate classification of mammograms using CNN needs a well-trained CNN by a large number of labeled mammograms. Unfortunately, a large number of labeled mammograms are not always available. In this study, a novel procedure to aid imaging specialists in detecting normal and abnormal mammograms has been proposed. The procedure supplied the designed CNN with a cheat sheet for some classical attributes extracted from the ROI and an extra number of labeled mammograms through data augmentation. The cheat sheet aided the CNN through encoding easy-to-recognize artificial patterns in the mammogram before passing it to the CNN, and the data augmentation supported the CNN with more labeled data points. Fifteen runs of 4 different modified datasets taken from the MIAS dataset were conducted and analyzed. The results showed that the cheat sheet, along with data augmentation, enhanced CNN’s accuracy by at least 12.2% and enhanced the precision of the CNN by at least 2.2. The mean accuracy, sensitivity, and specificity obtained using the proposed procedure were 92.1, 91.4, and 96.8, respectively, while the average area under the ROC curve was 94.9.
机译：美国癌症学会预计在美国诊断276,480例侵袭性乳腺癌新病例和2020年女性中的非侵入性乳腺癌新病例。早期发现乳腺癌，随后进行适当的治疗，可以降低这种疾病死亡的风险。 DL通过CNN可以协助成像专家在准确分类乳房X光线方面。使用CNN的准确分类乳房X线照片需要通过大量标记的乳房图来训练训练的CNN。不幸的是，许多标记的乳房X线照片并不总是可用的。在这项研究中，提出了一种援助成像专家检测正常和异常乳房X光图的新方法。该过程提供了设计的CNN，其中包含由ROI提取的一些经典属性的备忘单，以及通过数据增强的额外数量标记的乳房X线照片。在将其传递给CNN之前，备用表通过编码乳房X线图中的易于识别的人工图案来辅助CNN，并且数据增强支持CNN，其中具有更多标记的数据点。对来自MIS DataSet采取的4种不同修改的数据集进行了十五种，并分析。结果表明，备注板以及数据增强，增强了CNN的精度至少12.2％，并增强了CNN的精度至少为2.2。使用所提出的程序获得的平均准确性，灵敏度和特异性分别为92.1,91.4和96.8，而ROC曲线下的平均面积为94.9。

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