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首页> 外文会议>2012 Third International Conference on Computing Communication amp; Networking Technologies. >Automatic detection and classification of cancerous masses in mammogram
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Automatic detection and classification of cancerous masses in mammogram

机译：乳房X线照片中癌块的自动检测和分类

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摘要

Breast cancer is one of the most common forms of cancer in women. In order to reduce the death rate , early detection of cancerous regions in mammogram images is needed. The existing system is not so accurate and it is time consuming. The Proposed system is mainly used for automatic segmentation of the mammogram images and classifies them as benign, malignant or normal based on the decision tree ID3 algorithm. A hybrid method of data mining technique is used to predict the texture features which play a vital role in classification. The sensitivity, the specificity, positive prediction value and negative prediction value of the proposed algorithm were determined and compared with the existing algorithms. The size and the stages of the tumor is detected using the ellipsoid volume formula which is calculated over the segmented region. Automatic classification of the mammogram MRI images is done through three layered ANN .The weights are adjusted based on the rule extracted from ID3 algorithm .Both qualitative and quantitative methods are used to detect the accuracy of the proposed system.The sensitivity, the specificity, positive prediction value and negative prediction value of the proposed algorithm accounts to 99.78%, 99.9%, 94% and 98.5% which rates very high when compared to the existing algorithms. This paper focuses on the comparative analysis of the existing methods and the proposed technique in terms of sensitivity, specificity, accuracy, time consumption and ROC.
机译：乳腺癌是女性最常见的癌症形式之一。为了降低死亡率，需要尽早发现乳房X光照片中的癌变区域。现有系统不够精确，并且很费时间。提议的系统主要用于乳房X线图像的自动分割，并根据决策树ID3算法将其分类为良性，恶性或正常。数据挖掘技术的一种混合方法用于预测在分类中起着至关重要作用的纹理特征。确定了该算法的敏感性，特异性，正预测值和负预测值，并与现有算法进行了比较。使用椭圆体体积公式检测肿瘤的大小和分期，该公式是在分段区域上计算的。乳腺X线MRI图像的自动分类是通过三层神经网络进行的，权重是根据ID3算法提取的规则进行调整的，定性和定量方法均用于检测所提出系统的准确性，灵敏度，特异性，阳性所提算法的预测值和负预测值分别占99.78％，99.9％，94％和98.5％，与现有算法相比具有很高的评价率。本文着重从灵敏度，特异性，准确性，时间消耗和ROC方面对现有方法和拟议技术进行比较分析。

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