Ultrasonic-assisted liver lump diagnosis system and method

Abstract

The invention discloses an ultrasonic-assisted liver lump diagnosis system and method. The ultrasonic-assisted liver lump diagnosis system comprises an image preprocessing module, a sub-image segmentation module, an RGB channel separation module and a CNN network model construction module. The invention adopts a medical image anomaly detection method integrating the patch-based CNN, the class activation mapping and a faster R-CNN technology, and the method comprises the following steps of firstly, preprocessing an original image to remove the irrelevance; segmenting the original image into a plurality of sub-images, constructing a multi-layer CNN network model for each sub-image, and inputting the sub-images for recognition; connecting the networks of the sub-images as the final model identification result. Compared with the problems of inaccurate subjective judgment and low efficiency caused by the manual scanning judgment in the prior art, a liver abnormal lump image in an ultrasonic image can be automatically recognized, the lumps can be automatically identified for the benign and malignant analysis, and the method and the system are more accurate and efficient.

Description

technical field [0001] The invention relates to the technical field of medical diagnosis, in particular to an ultrasound-assisted diagnosis system and method for liver masses. Background technique [0002] There is currently no ultrasound-assisted diagnosis system for liver masses in the prior art, which are all manually scanned and judged. Due to subjective judgments and technical gaps, it is easy to cause misdiagnosis and misdiagnosis. [0003] In view of this, the present invention provides a medical image anomaly detection method that integrates patch-based CNN (convolutional neural network), class activation mapping and faster R-CNN technology, and uses deep transfer learning to perform effective feature learning without overfitting. This ensemble is an improvement over previous sliding window based efficiency methods. First, the original image is preprocessed to remove irrelevance; then the original image is divided into multiple sub-images, a multi-layer CNN network...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a system and method for ultrasound-assisted diagnosis of liver masses, which has the advantages of high efficiency and accurate inspection, and solves the problem of manual scanning judgment in the prior art. Due to subjective judgment and technical gap, it is easy to cause misdiagnosis and misdiagnosis. The problem

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