  The gold standard for gastric cancer detection is gastric histopathological
image analysis, but there are certain drawbacks in the existing
histopathological detection and diagnosis. In this paper, based on the study of
computer aided diagnosis system, graph based features are applied to gastric
cancer histopathology microscopic image analysis, and a classifier is used to
classify gastric cancer cells from benign cells. Firstly, image segmentation is
performed, and after finding the region, cell nuclei are extracted using the
k-means method, the minimum spanning tree (MST) is drawn, and graph based
features of the MST are extracted. The graph based features are then put into
the classifier for classification. In this study, different segmentation
methods are compared in the tissue segmentation stage, among which are
Level-Set, Otsu thresholding, watershed, SegNet, U-Net and Trans-U-Net
segmentation; Graph based features, Red, Green, Blue features, Grey-Level
Co-occurrence Matrix features, Histograms of Oriented Gradient features and
Local Binary Patterns features are compared in the feature extraction stage;
Radial Basis Function (RBF) Support Vector Machine (SVM), Linear SVM,
Artificial Neural Network, Random Forests, k-NearestNeighbor, VGG16, and
Inception-V3 are compared in the classifier stage. It is found that using U-Net
to segment tissue areas, then extracting graph based features, and finally
using RBF SVM classifier gives the optimal results with 94.29%.
