In this paper we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms is modeled after expert observers. In this work we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adaptively incorporating local proximity information into a classifier, it can help improve its classification accuracy, thereby leading to an improved "second opinion" to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.
They proposed to employ the fourth level of a Daubechies-orthogonal Wei et al. REF suggested a microcalcification classification using an adaptive support vector machine (SVM).
Microcalcification Classification Assisted by Content-Based Image Retrieval for Breast Cancer Diagnosis
2007 IEEE International Conference on Image Processing
2007 IEEE International Conference on Image Processing
Computer Science Medicine