A bladder tumor detection method based on MaskRcnn

Abstract

The invention provides a bladder tumor detection method based on MaskRcnn, At least that method comprise: a few pictures of medical bladder are collected as training samples, An image processing method is used to solve the problem of over-fitting caused by inadequate medical bladder training pictures which generate unbalanced sample data. The picture has a specific rectangular recognition frame which contains bladder position marker or tumor position marker or has both bladder and tumor position marker. A medical picture is characterized by utilizing a brand-new paramete ResNet debugged according to that invention, and a predicted ROI region is generate; The predicted ROI region represents the possible location of the bladder and tumor; The training speed and testing speed are effectivelyimproved by weighting ResNet operation. The coordinate translation and scale scaling of the region of interest are carried out by using the edge of the border. A target rectangular frame is obtained by using a non-maximum resistance. The invention uses artificial intelligence method, namely deep learning, which greatly reduces the repetitive manual operation of the traditional method and improvesthe speed and efficiency. An instanteous function is achieved.

Description

technical field [0001] The invention relates to a method in the technical field of artificial intelligence detection, in particular to a method for detecting bladder tumors based on MaskRcnn. Background technique [0002] In recent years, with the rapid development of artificial intelligence, more and more experts have devoted themselves to the research of deep learning. In the field of computer recognition, there are advanced and excellent results every year that have attracted the attention of scholars and scientists around the world. At the same time, Stanford University, Google, Microsoft, Facebook and other top Ai research laboratories around the world participate in the large-scale computer recognition challenge every year. Some data sets such as ImageNet, Pascal Voc, Microsoft Coco and other large data sets have become the challenge targets of many researchers, so Many excellent articles and advanced algorithm networks have been born for reference by scholars. [000...

AI Technical Summary

Problems solved by technology

[0011] Collect medical bladder pictures as training samples, and perform data enhancement on the training samples to solve the problem of overfitting during the test process. The picture has a specific rectangular recognition frame, which contains bladder position markers or tumor position markers or both bladder and tumor location markers;

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