计算机辅助下自体荧光图像定量结果与口腔白斑病上皮异常增生等级的相关性
Correlation between computer-assisted quantitative autofluorescence imaging results and the pathological grading of oral epithelial dysplasia in oral leukoplakia
Note： A. Confusion matrix of the test set prediction model for binary classification of pathological grade. Darker color blocks along the diagonal indicate a higher probability of correct predictions. B. Confusion matrix of the test set prediction model for four-class classification of pathological grade. Dark color blocks are primarily concentrated in the second and third column, indicating that the model tends to predict the degree of epithelial dysplasia as mild or moderate. C. Decision tree of the training set for binary classification of pathological grade, showing the prediction process of each sample. The red circle indicates the root node, and the red square indicates the leaf nodes, with different classification results. D. Decision tree of the training set for four-class classification of pathological grade, showing the prediction process of each sample. The red circle indicates the root node, and the red square indicates the leaf nodes, with different classification results.
