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首页> 外文会议>International Conference on Electrical and Computer Engineering >Parametric Image-based Breast Tumor Classification Using Convolutional Neural Network in the Contourlet Transform Domain
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Parametric Image-based Breast Tumor Classification Using Convolutional Neural Network in the Contourlet Transform Domain

机译：基于参数图像的乳腺肿瘤分类在Contourlet变换域中使用卷积神经网络进行分类

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Automated visual identification of Benign and Malignant breast tumors even with ultrasound (US) B-Mode image is still an open area of research. This paper presents parametric image-based approach to classify and detect benign and malignant breast tumors from ultrasound images using a custom-made convolutional neural network architecture. The Rician Inverse Gaussian (RiIG) distribution is presented here as a suitable model for describing the statistics of the ultrasound images in the Contourlet Transform domain. Locally computed values of the dispersion parameters of the RiIG distribution in various contourlet sub-bands yield parametric images those are classified using the proposed convolutional neural network. Experiments are conducted on a publicly available dataset of 250 images, of 100 belong to the benign Fibroadenoma and 150 in the malignant category. The proposed method provides accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) of 96%, 97.3%, 94.12%, 96% and 96%, respectively. It is also shown that the accuracy obtained by the Proposed Method is higher than several recently reported results.
机译：即使用超声波（US）B模式图像也是自动视觉鉴定良性和恶性乳腺肿瘤仍然是一个开放的研究领域。本文介绍了基于参数的图像的方法，用于使用定制的卷积神经网络架构来分类和检测来自超声图像的良性和恶性乳腺肿瘤。瑞典逆高斯（RIIG）分布如此作为合适的模型，用于描述Contourlet变换域中超声图像的统计。各种Contourip子带中的RIIG分布的分散参数的局部计算值屈服参数图像使用所提出的卷积神经网络分类。实验在公共可用的数据集中进行了250张图像，100个属于恶性类别的良性纤维腺瘤和150个。所提出的方法为分别提供准确性，敏感性，特异性，阳性预测值（PPV），以及分别为96％，97.3％，94.12％，96％和96％的负预测值（NPV）。还表明，通过所提出的方法获得的准确性高于最近报道的结果。

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