| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 8
| Issue : 1 | Page : 15 |
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Application of graph-based features in computer-aided diagnosis for histopathological image classification of gastric cancer
Haiqing Zhang1, Chen Li1, Shiliang Ai1, Haoyuan Chen1, Yuchao Zheng1, Yixin Li1, Xiaoyan Li2, Hongzan Sun2, Xinyu Huang3, Marcin Grzegorzek3
1 Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
2 Liaoning Cancer Hospital and Institute, Shengjing Hospital, China Medical University, Shenyang, China
3 Institute of Medical Informatics, University of Luebeck, Luebeck, Germany
Correspondence Address:
Chen Li
Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang
China
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/digm.digm_7_22
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Background: The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. Method: In this paper, based on the study of computer-aided diagnosis (CAD) 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. 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. Result: 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%. Conclusion: This paper focus on a graph-based features microscopic image analysis method for gastric cancer histopathology. The final experimental data shows that our analysis method is better than other methods in classifying histopathological images of gastric cancer.
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