Automatic identification system of pancreatic cancer based on deep learning, computer equipment, storage medium

Abstract

The invention discloses a pancreatic cancer tumor automatic identification system based on depth learning, belonging to the technical field of image identification. The system comprises a depth learning model, wherein the depth learning model comprises a feature extraction network, a region generation network and a fast R- CNN target detection network; The feature extraction network is used for abstracting the image features of pancreatic cancer tumors and generating a convolution feature map; The region generation network is used for sliding scanning all features existing in the convolution feature map, and selecting a plurality of candidate regions at each sliding window position, wherein the candidate regions are possible pancreatic cancer tumor regions; Fast R-CNN target detection network is used to classify and regress the convolution feature map and the generated candidate regions, and finally output the location and probability of the pancreatic cancer tumor region. The system of the invention can complete the tracking identification of the pathological tissue, reduce the manual operation, and has the advantages of high processing speed and high accuracy.

Description

technical field [0001] The present invention relates to the technical field of image recognition, in particular to an automatic recognition system for pancreatic cancer tumors based on deep learning, computer equipment, and storage media. Background technique [0002] The pancreas is a retroperitoneal organ with a deep anatomical location and complex surrounding structures, making it difficult to diagnose. With the continuous development and improvement of imaging technology in recent years, it plays an important role in the diagnosis, staging and prognosis of pancreatic cancer. In particular, CT has high spatial resolution and density resolution without overlapping anatomical structures. The most important imaging method for pancreatic cancer. [0003] In traditional diagnosis, professional physicians observe the images, compare and analyze a series of images of cases, and rely on experience to extract and mark pancreatic tumors. This method requires professional doctors ...

AI Technical Summary

Problems solved by technology

This method requires professional doctors to perform cumbersome manual operations on a large amount of data. At the same time, the accuracy and reliability of the diagnostic results of this method are heavily dependent on the doctor's experience, knowledge and professional quality, and the accuracy of the diagnostic results is limited.

Images