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首页> 外文会议>International Workshop on Breast Imaging >Automatic classification of clustered microcalcifications in digitized mammogram using ensemble learning
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Automatic classification of clustered microcalcifications in digitized mammogram using ensemble learning

机译：使用集合学习自动分类数字化乳房X线图中的聚类微钙化

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Microcalcifications (MC) are small deposits of calcium, which are associated with early signs of breast cancer. In this paper, a novel approach is presented to develop a computer-aided diagnosis (CADx) system for automatic differentiation between benign and malignant MC clusters based on their morphology, texture, and the distribution of individual and global features using an ensemble classifier. The images were enhanced, segmented and the feature extraction and selection phase were carried out to generate the feature space which was later fed into an ensemble classifier to classify the MC clusters. The validity of the proposed method was investigated by using two well-known digitized datasets that contain biopsy proven results for MC clusters: MIAS (24 images: 12 benign, 12 malignant) and DDSM (280 images: 148 benign and 132 malignant). A high classification accuracies (100% for MIAS and 91.39% for DDSM) and good ROC results (area under the ROC curve equal to 1 for MIAS and 0.91 for DDSM) were achieved. A full comparison with related publications is provided. The results indicate that the proposed approach is outperforming the current state-of-the-art methods.
机译：微钙化（MC）是钙的沉积小，这是与乳腺癌的早期迹象相关联。在本文中，提出了一种新颖的方法来开发一种计算机辅助诊断（的CADx），用于根据它们的形态，质地良性和恶性MC簇之间自动微分，和个别的和全局特征使用集成分类分配系统。图像增强时，分割和特征提取和选择阶段进行了生成以后被送入集成分类到MC簇分类特征空间。 MIAS（24个图像：12良性，恶性12）和DDSM（280个图像：148良性和恶性132）所提出的方法的有效性是通过使用两个包含活检证实结果MC簇公知的数字化的数据集的影响。高分类精确度（100％为MIAS和DDSM 91.39％）和良好的ROC结果（ROC曲线下面积等于1 MIAS和0.91 DDSM）得以实现。提供有相关出版物全面的比较。结果表明，所提出的方法的效果明显超过当前状态的最先进的方法。

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