使用深度学习特征的乳房x线照片分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI:10.1109/ICSIPA.2017.8120660

S. J. S. Gardezi, M. Awais, I. Faye, F. Mériaudeau

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引用次数: 32

摘要

本文提出了一种使用深度学习方法对乳房x线照片中的正常和异常组织进行分类的方法。在IRMA数据集的乳房x线照片roi上实现了带有卷积滤波器(3×3)的VGG-16 CNN深度学习架构。深度特征矩阵从第一个全连通层开始计算。使用支持向量机、二叉树、简单物流和KNN (k= 1,3,5)分类器对结果进行10次交叉验证。该方法的分类准确率为100%，AUC为1.0。

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Mammogram classification using deep learning features

This paper presents a method for classification of normal and abnormal tissues in mammograms using a deep learning approach. VGG-16 CNN deep learning architecture with convolutional filter of (3×3) is implemented on mammograms ROIs from the IRMA dataset. The deep feature matrix is computed from first fully connected layer. The results are evaluated using 10 fold cross validation on SVM, binary trees, simple logistics and KNN (with k=1, 3, 5) classifiers. The method produced 100% classification accuracies with AUC 1.0.

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2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

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