基于极限学习机特征的乳腺癌调查

Zhiqiong Wangi, Hanyu Jiang
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引用次数: 0

摘要

极限学习机算法,它是一个基于优化的学习框架,用于压缩、特征、聚类、回归和分类。为了克服这些问题,提出了一种人工隐藏节点,并通过唯一最小解提供更好的泛化性能。本文介绍了基于卷积神经网络(CNN)特征的计算机辅助诊断(CAD),它们基于深度特征、形态特征、纹理特征和密度特征。乳房x线摄影的概念被更多地用于早期发现乳腺癌,可以帮助CAD系统判断其是恶性还是良性,通过乳房x线摄影图像来区分正常或异常组织。本文的主要目的是在该算法中检测乳腺癌组织的阶段是有害的还是无害的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survey on Breast Cancer Based on Extreme Learning Machine Features
Extreme Learning Machine algorithm which it is an optimization based learning framework for compression, feature, clustering, regression and classification. It is an artificial hidden nodes and proposed to overcome these issues and offer better generalization performance by Unique minimum solution. In this paper Computer-aided diagnosis (CAD) based on Convolutional Neural Network (CNN) feature and they are based on deep, morphological, texture and density features. Mammography concept is been more used and to detect early stage of breast cancer and can helps out whether they are Malignant or Benign in CAD system they are classified by normal or abnormal tissue with the help of mammography images. The main objective of this paper is to detect the stage of breast cancer tissue is harmful or not in this algorithm.
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