Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some drawbacks.This study explored a computer-aided diagnostic method that can be used to identi...Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some drawbacks.This study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological images.Methods The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for classification.First,the U-net was applied to segment the image.Next,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was drawn.The third step was to extract the graph-curvature features of the histopathological image according to the MST image.Finally,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be obtained.Results During the experiment,we used various methods for comparison.In the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were used.We found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological images.In the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern feature.In the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and inception-V3.Through comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.82220108007).
文摘Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some drawbacks.This study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological images.Methods The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for classification.First,the U-net was applied to segment the image.Next,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was drawn.The third step was to extract the graph-curvature features of the histopathological image according to the MST image.Finally,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be obtained.Results During the experiment,we used various methods for comparison.In the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were used.We found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological images.In the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern feature.In the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and inception-V3.Through comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.
