The present invention pertains to a
cancer cell identification method and a
cancer cell identification system capable of identifying
cancer cells via CTC detection by using an unstained image, and simply and highly accurately identifying and classifying cancer cells. The present invention comprises a CGAN 12 that performs
image generation learning for generating a
fluorescence estimation image 18 obtained by learning, in pairs, a
fluorescence-stained image 8, which is obtained by fluorescently
staining an inspection
cell 2 via antibodies, and an unstained image 16 thereof, and then estimating the
fluorescence-stained image 8 from the unstained image 16. The present invention also comprises a CNN 14 that performs
image identification learning for receiving input of the unstained image 16 and the fluorescence
estimation image 18 corresponding thereto as one set of cell information, performing
machine-learning in advance as to whether the unstained image 16 is a
cancer cell by using the fluorescence-stained image 8 of the inspection cell 2, and identifying whether the input unstained image 16 is a
cancer cell. The CGAN 12 generates the fluorescence
estimation image 18 from the unstained image 16, and the CNN 14 identifies whether the unstained image 16 obtained by imaging an inspected cell in blood is a
cancer cell from the fluorescence estimation image 18 on the basis of the
image identification learning, and then classifies the cell.