Abstract
The purpose of this study is to develop a diagnostic support system for anomaly detection from pathology image using HLAC features and SVM. In this paper, the optimization results of three parameters, which are image size of subspace to extract HLAC features, number of learning data, and kernel function for SVM, are reported. The optimal parameters are calculated by compare the correct detection rate of the proposed method with the diagnosis by a pathologist. The experimental results show that the correct detection rate depends on image size of subspace to extract features and number of learning data, the correct detection rate of anomaly cells decreases with an increase of the learning data, and the correct detection rate of normal cells increases with an increase of the learning data.
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