Integrated ResNet-NRC method for dividing sample space based on lung tumor image

The invention discloses an integrated ResNet-NRC method for dividing a sample space based on a lung tumor image, and the method comprises the following steps: collecting medical image information of the same lung tumor in three dimensions, i.e., a three-mode data set CT, a PET and a PET/CT; dividing sample feature spaces of three modes according to the three-mode data set CT, PET and PET/CT; constructing residual neural network models of the three modes according to the sample feature spaces of the three modes, namely a base classifier; and combining the three base classifiers by adopting a relative majority voting method to form a final classification identification result. According to the method, the classification accuracy is excellent, the conditions of high accuracy and large difference of a base classifier are met, the optimization problem of high-dimensional data can be effectively solved, the specificity, sensitivity and other evaluation indexes are high, and the method has good robustness and genera