A cervical image processing method and device based on a dense feature pyramid network

Abstract

The invention discloses a cervical image processing device based on a dense feature pyramid network, including an image acquisition device for collecting cervical images processed by 3%-5% acetic acid solution; a processor, including a cervical image preprocessing module and A processing module, the processing module includes a model network composed of a densely connected feature pyramid network, a region nomination network and a detection network, used to output classification information and position information of the target region; memory, used to store the model network in the processor parameters; a display device for displaying the classification information and location information of the target area output by the processor. Also disclosed is a cervical image processing method based on a dense feature pyramid network, comprising: inputting the cervical image collected by the image acquisition device and processed by 3%-5% acetic acid solution into the model network trained by the processor, and outputting the image of the target area. Classification information and location information are displayed on the display device.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a cervical image processing method and device based on a dense feature pyramid network. Background technique [0002] Deep learning is a method based on representation learning of data in machine learning. Observations can be represented in a variety of ways, such as a vector of intensity values ​​for each pixel, or more abstractly as a series of edges, regions of a specific shape, etc. And it is easier to learn tasks from examples with some specific representations. The advantage of deep learning is to use unsupervised or semi-supervised feature learning and hierarchical feature extraction efficient algorithms to replace manual feature acquisition. [0003] With the continuous fermentation of deep learning research in recent years, more and more application scenarios focusing on image recognition have begun to appear in deep learning, including medical image recogni...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problem in the prior art that the cervical image treated with 3%-5% acetic acid solution has "vinegar white" characteristics in many areas, and it is difficult to distinguish between normal "vinegar white" and lesion "vinegar white", the present invention Provides a cervical image processing method and device based on a dense feature pyramid network for distinguishing normal "vinegar white" from lesion "vinegar white" in cervical images

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