An automatic chromosome counting method based on depth learning

The invention discloses a chromosome automatic counting method based on depth learning, which comprises the following steps: (1) image collection and preprocessing steps; (2) image classification andregression steps; (3) model training steps; (4) a test counting step, wherein a new sampling strategy is adopted in the step (2), and Faster R-CNN loss function model is improved. The data required bythe invention comes from G-banded chromosomes under a real microscope field of vision, and the method does not need a complex experiment process, has low cost and shorter time consumption, and can automatically and accurately complete the target chromosome counting. The invention uses 1000 examples of annotated chromosome map training model, and then uses 175 examples of annotated chromosome mapfor testing, statistics shows that 175 examples contain 8023 chromosomes, the accuracy of testing is 98.95%, recall rate is 98.67%. Test results show that the time required to complete a chromosome count report using the machin