Liver CT image multi-lesion classification method based on sample generation and transfer learning

The invention discloses a liver CT image multi-lesion classification method based on sample generation and transfer learning. The method mainly solves the problem that an existing method is not high in liver multi-lesion detection performance. The implementation scheme is as follows: dividing a data set; respectively constructing a liver organ segmentation network and a liver lesion detection network; based on the deep convolution generative adversarial network, constructing a liver cyst sample generation network and a liver hemangioma sample generation network, and respectively generating newliver cyst and liver hemangioma samples; constructing a liver lesion classification network; subjecting a liver CT image to be detected firstly to organ segmentation by using a liver organ segmentation network, then subjecting a segmentation result to lesion detection by using a liver lesion detection network, and finally classifying detected lesions by using a liver lesion classification network. According to the invent