American Journal of Applied Sciences | |
Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning | Science Publications | |
P. N. Hashimah1  Aidarus M. Ibrahim1  Baharum Baharudin1  Abas Md Said1  | |
关键词: Mammogram; Breast Cancer; K-Means Clustering; Max-Pooling; Bag-of-Features; Classifier; | |
DOI : 10.3844/ajassp.2016.552.561 | |
学科分类:自然科学(综合) | |
来源: Science Publications | |
【 摘 要 】
In this study, we propose a learning-based approach using feature learning to minimize the manual effort required to extract features. Firstly, we extracted features from equally spaced sub-patches covering the input Region of Interest (ROI). The dimensionality of the extracted features is reduced using max-pooling. Furthermore, spherical k-means clustering coupled with max pooling (k-means-max pooling) is compared with well-known feature extraction method namely Bag-of-features. The resulting feature vector is fed to two different classifiers: K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of these classifiers is evaluated to use of Receiver Operating Characteristics (ROC). Our results show that k-means-max pooling, combined with K-NN, achieved good performance with an average classification accuracy of 98.19%, sensitivity of 97.09% and specificity of 99.35%.
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
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RO201911300998164ZK.pdf | 498KB | download |