Gastric Lymph Node Cancer Detection of Multiple Features Classifier for Pathology Diagnosis Support System
Takumi Ishikawa, Junko Takahashi, Hiroshi Takemura, Hiroshi Mizoguchi, Takeshi Kuwata · 2013
In this paper, an automatic cancer detection method that combines multiple features to support pathologists was proposed. Cancer is the most cause of death in Japan, and patients suffering with cancer are increasing every year, while the number of pathologists is almost constant. Such issues increase the burden on the pathologists and causes service degradation for the patients. The proposed method combined three image features, Higher-order Local Auto-Correlation (HLAC) feature, Wavelet feature, Delaunay feature. At first, the features were calculated from gastric lymph node images. Then we connected each feature into each vector of varying combinations of the features, and discriminated cancer and no cancer by Support Vector Machine (SVM). HLAC, Wavelet and Delaunay features are shape, frequency, and cell-position geometrical one respectively. Cancer detection rates with more than two features combination were better than only one. In the best performance, sensitivity and specificity were 94.6% and 84.9% respectively.