The identification of cervical abnormal cells using deep learning methods usually requires a large amount of training data

but these data inevitably use different samples of cervical abnormal cells to participate in model training

and naturally miss the positive and abnormal intracellular controls of a single sample

resulting in the fact that recognition accuracy of cervical abnormal cells is not high

and the false positive rate is high. To solve this problem

this paper proposes a method for identifying cervical abnormal cells based on sample benchmark values. Firstly

this method identifies cervical cells and segments cervical cell nucleus by Mask R-CNN model. Then we calculate the key cervical nucleus indicators

propose the concept of benchmark cells

define the sample benchmark values

and quantify the diagnostic criteria.Finally

the abnormal nucleus indicator and model information are used to complete the reclassification of abnormal cervical cells

and the abnormal cells were identified by simulating a doctor comparing the morphology of the normal cells in the sample. Experiments show that the positive cell completion rate

positive cell detection accuracy and sample detection accuracy rate on the cervical cell smear dataset reached 84.7%

94.6% and 92.4%

respectively.