Currently

there are two challenges in identifying abnormal cervical cells using deep learning: (1) cervical cells are diverse and cervical cell images vary from person to person. (2 ) Cervical cells show long-tailed distribution

which affects the classification accuracy of cervical cells. In this paper

a deep learning-based cervical cell segmentation and classification framework is proposed. This framework first performs cell nucleus segmentation

uses U-Net as the base model for layer reduction

adds AG module

and uses ACBlock module instead of traditional standard convolutional blocks; then uses ResNeSt for coarse classification of segmented data

fuses manual features extracted based on physicians ′ experience and machine features extracted by ResNeSt network for fine classification

and uses active learning iteratively to expand the cervical cell categories and fuse the ACBlock module in the BBN model to process the long-tail data; finally

the diagnostic indexes of abnormal cells are refined and abnormal cells are screened according to the TBS diagnostic criteria and the physician ′s diagnostic experience. Experiments show that the segmentation algorithm in this paper improves 3. 52% compared with the original method

and the classification algorithm that incorporates all features improves 1. 2% . For positive patients

the accuracy of cancer cell diagnosis reached 91% .