Cervical abnormal cell detection method and system

Abstract

The invention discloses a cervical abnormal cell detection method and system, and relates to neural network detection of abnormal cells. The method comprises the following steps: establishing an abnormal cell detection model, and labeling a cervical slide image as a labeled image containing pathological information; segmenting the annotated image into block images; respectively performing multi-scale transformation on the block images to respectively obtain scale images, and in a coordinate system established by taking the upper left corner of each scale image as an original point, marking real position coordinates and pathological marking information corresponding to each marking box; performing color normalization processing on the scale images to obtain normalized images; screening outeffective annotation images of which the annotation box widths and heights are smaller than those of the filter boxes from the normalized images; using a fixed frame to select the image area with themost tagging frames in the effective tagging images as a training image to obtain an abnormal cell detection model; the method has the beneficial effect that the detection accuracy is greatly improvedby carrying out multi-scale transformation on the cervical slide image.

Description

Technical field [0001] The invention relates to the field of neural network detection of abnormal cells, in particular to a method and system for detecting abnormal cells of the cervix. Background technique [0002] Cervical cancer is the most threatening malignant tumor disease to women. With the development of cervical cancer screening methods, cervical cancer is currently the only cancer that can be detected and cured in advance. The current screening method mainly relies on doctors for real-time reading under the microscope and evaluation of the case. Although this method is reliable, it depends on the examiner's professional level. At the same time, when the number of readings increases, fatigue and the doctor's subjective recognition will affect the accuracy of reading. [0003] In recent years, the detection of abnormal cervical cells by deep learning neural networks has solved this problem to a certain extent. The method of detecting abnormal cells of the cervix with the ...

AI Technical Summary

Problems solved by technology

However, at present, the image scale of the cervical slide that can be recognized by the neural network is single, and the clarity and color of the image itself affect the accurate judgment of the neural network on the abnormal cells of the cervix, which are often not recognized by the neural network. It is particularly important to perform multi-scale transformation on the slide image to train the recognition ability of the neural network, so as to improve the accuracy of abnormal cervical cell detection.

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