论文标题
使用加权功能支持向量机的基于功能的乳房X线图图像分类
Features based Mammogram Image Classification using Weighted Feature Support Vector Machine
论文作者
论文摘要
在现有的乳房X光图像分类研究中，考虑到特定类型的临床数据或图像特征以及有监督的分类器（例如神经网络（NN）和支持向量机）（SVM）。本文将乳腺组织类型的自动分类视为良性或恶性，使用加权特征支持向量机（WFSVM），通过使用最大化偏差的原理为相关特征分配更多的权重，通过构建预报的核函数。最初，将乳房X线图图像的MIA数据集分为训练和测试集，然后将诸如降噪和背景删除之类的预处理技术应用于输入图像，并确定了感兴趣的区域（ROI）。统计特征和纹理特征是从ROI中提取的，临床特征是直接从数据集获得的。训练数据集的提取功能用于构建加权功能和预先计算的线性内核，以训练WFSVM，从中创建了训练模型文件。使用此模型文件，测试样品的内核矩阵分为良性或恶性。该分析表明，与WFSVM和SVM的其他功能相比，纹理特征的准确性更好。但是，WFSVM中创建的支持向量的数量小于SVM分类器。
In the existing research of mammogram image classification, either clinical data or image features of a specific type is considered along with the supervised classifiers such as Neural Network (NN) and Support Vector Machine (SVM). This paper considers automated classification of breast tissue type as benign or malignant using Weighted Feature Support Vector Machine (WFSVM) through constructing the precomputed kernel function by assigning more weight to relevant features using the principle of maximizing deviations. Initially, MIAS dataset of mammogram images is divided into training and test set, then the preprocessing techniques such as noise removal and background removal are applied to the input images and the Region of Interest (ROI) is identified. The statistical features and texture features are extracted from the ROI and the clinical features are obtained directly from the dataset. The extracted features of the training dataset are used to construct the weighted features and precomputed linear kernel for training the WFSVM, from which the training model file is created. Using this model file the kernel matrix of test samples is classified as benign or malignant. This analysis shows that the texture features have resulted in better accuracy than the other features with WFSVM and SVM. However, the number of support vectors created in WFSVM is less than the SVM classifier.
