The application relates to an intelligent diagnosis method for
cervical cell atrophy levels, and relates to the problem that intelligent discrimination of
cervical cell atrophy degrees is lacking in automatic
pathological diagnosis technology. Cervical vaginal squamous
epithelium is divided into
surface layer cells, middle layer cells and basal layer cells, and ovarian
estrogen affects the growth and maturity of the cells; a decrease in
estrogen level can cause symptoms such as atrophic
vaginitis and
osteoporosis, which need to be treated in time; at present, squamous
cell atrophy diagnosis is not clear enough, and there are few reports on atrophy degree diagnosis research, so it is of great significance to establish a systematic and intelligent diagnosis process for
cervical cell atrophy degree discrimination. In order to improve the problem, the application provides an intelligent diagnosis method for cervical
cell atrophy levels; the method first detects the
surface layer cells, the middle layer cells and the basal layer cells by using a target detection model, then segments the
cell nucleus of each layer of cells detected by using an instance segmentation model, and finally calculates the cell quantity ratio, the
nucleus-
cytoplasm ratio and the cell
crowding degree index of each layer; the indexes are input into a
random forest classification model to grade the atrophy degree; it is known through sufficient experimental
verification that good effects are achieved in cervical cell atrophy degree discrimination. The application is applied to cervical cell atrophy degree discrimination.