Study on cascade classification in abnormal shadow detection for mammograms
M Nemoto, A Shimizu, H Kobatake, H Takeo… - … Workshop on Digital …, 2006 - Springer
Classifier plays an important role in a system detecting abnormal shadows from
mammograms. In this paper, we propose the novel classification system that cascades four
weak classifiers and a classifier ensemble to improve both computational cost and
classification accuracy. The first several weak classifiers eliminate a large number of false
positives in a short time which are easy to distinguish from abnormal regions, and the final
classifier ensemble focuses on the remaining candidate regions difficult to classify, which …
mammograms. In this paper, we propose the novel classification system that cascades four
weak classifiers and a classifier ensemble to improve both computational cost and
classification accuracy. The first several weak classifiers eliminate a large number of false
positives in a short time which are easy to distinguish from abnormal regions, and the final
classifier ensemble focuses on the remaining candidate regions difficult to classify, which …
Study on Cascade Classification in Abnormal Shadow Detection for Mammograms
A Shimizu, M Nemoto, H Kobatake, S Nawano… - (No Title), 2006 - cir.nii.ac.jp
Classifier plays an important role in a system detecting abnormal shadows from
mammograms. In this paper, we propose the novel classification system that cascades four
weak classifiers and a classifier ensemble to improve both computational cost and
classification accuracy. The first several weak classifiers eliminate a large number of false
positives in a short time which are easy to distinguish from abnormal regions, and the final
classifier ensemble focuses on the remaining candidate regions difficult to classify, which …
mammograms. In this paper, we propose the novel classification system that cascades four
weak classifiers and a classifier ensemble to improve both computational cost and
classification accuracy. The first several weak classifiers eliminate a large number of false
positives in a short time which are easy to distinguish from abnormal regions, and the final
classifier ensemble focuses on the remaining candidate regions difficult to classify, which …
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