Abstract：Objective To establish a random forest model based on target region segmentation and feature extraction of the dataset so as to
classify the risk of early lung cancer. Methods The BiSeNet algorithm was used to realize image segmentation, and the segmented image was
converted to YCbCr color space model. The non-skin-color points were found through the values of CB and CR components, and the non-skincolor
points were filtered by 9×9 mean filter. The color feature value was extracted under the color model, then the image was converted to
gray space. The texture feature value was obtained on the gray level co-occurrence matrix. These eigenvalues were used as inputs to construct
a random forest classification model, and ID3 algorithm was used to construct a decision tree. The optimal classification model was found
by adjusting the number of decision tree and the maximum eigenvalues. Results The segmentation accuracy of BiSeNet facial image was
96.25%. In YCbCr color space, it had the feature of elliptical skin color clustering, and non-skin-color points could be detected. After adjusting
the two parameters (the number of decision tree and the maximum eigenvalues), it is found that when the values of the two parameters were 30
and 4 respectively, the random forest model had the best performance, and the accuracy rate could reach 87.34%. Conclusion The early lung
cancer can be classified according to the facial color features and texture features. The experimental analysis shows that the facial red features
and texture features of the patients with lung cancer are significantly different from those of patients without lung cancer, which contribute
to realize the classification and judgment of early lung cancer, and provide auxiliary evidence for the clinical discovery of early lung cancer.
周孟齐a，胡广芹a，林岚a，李斌a，张新峰b. 基于面部图像的有无早期肺癌风险分类研究[J]. 中国医疗设备, 2022, 37(11): 52-56.
ZHOU Mengqia, HU Guangqina, LIN Lana, LI Bina, ZHANG Xinfengb. Study on Risk Classification of Early Lung Cancer Based on Facial Images. China Medical Devices, 2022, 37(11): 52-56.
