基于深度卷积神经网络的乳腺癌组织病理学图像分类

Steve A. Adeshina, A. P. Adedigba, A. A. Adeniyi, A. Aibinu
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引用次数: 29

摘要

这项工作解决了乳腺组织病理学图像的类内分类问题,分为八(8)类良性或恶性细胞。目前的人工特征提取和分类充满了不准确性,导致高假阴性率和随之而来的死亡率。深度卷积神经网络(Deep Convolutional Neural Networks, DCNN)在图像分类中已被证明是有效的。我们采用DCNN架构结合集成学习方法,使用带反向传播训练和ReLU激活函数的TensorFlow框架实现对这些图像的准确自动分类。我们使用BreakHis数据集实现了91.5%的类间分类准确率。
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Breast Cancer Histopathology Image Classification with Deep Convolutional Neural Networks
This work addresses the problem of intra-class classification of Breast Histopathology images into Eight (8) classes of either Benign or Malignant Cell. Current manual features extraction and classification is fraught with inaccuracies leading to high rate false negatives with attendant mortality. Deep Convolutional Neural Networks (DCNN) have been shown to be effective in classification of Images. We adopted a DCNN architecture combined with Ensemble learning method using TensorFlow Framework with Backpropagation training and ReLU activation function to achieve accurate automated classification of these Images. We achieved inter-class classification accuracy of 91.5% with the BreakHis dataset.
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