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Characterization Breast Cancer Histology Images using Deep Learning

机译:使用深度学习表征乳腺癌组织学图像

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The paper employs deep learning to classify breast cancer histopathological image into normal, benign and malignant subclasses in situ carcinoma and invasivecarcinoma categories. The classification is mainly based on cells' density, variability, and organization along with overall tissue structure and morphology. Smaller and larger patches of histological images are extracted that includes cell-level and tissue-level features. Here, Patches are screened by Clustering algorithm and CNN is used to select the discriminative patches. The proposed approach is applied to the multi-class classification of breast cancer histology images.It achieves initial test achieves of 95% accuracy and on the overall test,88.89% accuracy.
机译:本文采用深入的学习,将乳腺癌组织病理学图像分类为正常,良性和恶性亚类,原位癌和侵入性癌症类别。分类主要基于细胞的密度,可变性和组织以及整体组织结构和形态。提取包括细胞级和组织级别特征的较小和更大的组织学图像斑块。这里,通过聚类算法筛选补丁,使用CNN来选择辨别斑块。所提出的方法适用于乳腺癌组织学镜片的多阶级分类。初始测试达到95%的精度和总体测试,精度为88.89%。

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