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首页> 外文会议>IEEE International Conference on Big Data >Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Features for Mammography Mass Classification
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Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Features for Mammography Mass Classification

机译：基于分布式粗糙集的特征选择方法，分析乳房X线觉摄量分类的深和手工制作功能

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Breast cancer has a high incidence among women worldwide. This, together with the recent developments in deep learning based convolutional networks, have motivated research towards the enhancement of Computer Aided Diagnosis (CAD) systems. In this paper, the performance of a densely connected convolutional network (DenseNet) for breast cancer was investigated for the malignant/benign classification of mammographic masses. Different mammography data sets were collected to investigate the capacity of this network for learning a combination of these databases. To achieve this, internal low-level, mid-level and high-level features/abstracts were extracted from the model together with hand-crafted features, generating a vast amount of data. Using the distributed rough set based feature selection approach (Sp-RST), significant features were selected from both deep learning based features and hand-crafted ones, and fed into a learning model with separate and combined data approaches for the classification of mammographic masses. Results show that by using Sp-RST as a powerful technique capable of performing big data preprocessing, DenseNet had the representational capacity to learn mammographic abnormalities.
机译：乳腺癌在全球妇女的发病率很高。这与最近基于深度学习的卷积网络的发展，具有推动计算机辅助诊断（CAD）系统的激励研究。本文研究了乳腺癌的恶性/良性分类的乳腺癌密集连接的卷积网络（DENSENET）的性能。收集不同的乳房X线术数据集以研究该网络学习这些数据库组合的能力。为实现这一目标，从模型中提取内部低级，中级和高级功能/摘要以及手工制作的功能，产生大量数据。使用基于分布式粗略集合的特征选择方法（SP-RST），从基于深度学习的特征和手工制作的特征和手工制作的特征选择方法，并进入学习模型，其中具有单独的和组合的数据方法来分类乳房肿块。结果表明，通过使用SP-RST作为能够进行大数据预处理的强大技术，DenSenet具有学习乳房观察异常的代表性能力。

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