Image recognition method and device, electronic equipment and storage medium

Abstract

The invention provides an image recognition method and device, electronic equipment and a storage medium. The method comprises the following steps: firstly, acquiring an initial image of medical scanning corresponding to a target part, and then preprocessing the initial image to obtain at least one of a post-processing image of a blood vessel in the target part and an abnormal region in which a blood vessel center line parameter exists; and performing focus identification based on the initial image and the post-processing image of the blood vessel to obtain a focus area, and determining and identifying a focus image in the initial image according to the focus area and the abnormal area to obtain a focus identification result. According to the method and device, focus identification is performed on the initial image by adopting multiple ways, so that omission of manual identification is made up, and the accuracy of an identification result is improved.

Description

technical field [0001] The present application relates to the technical field of medical image processing, and in particular to an image recognition method, device, electronic equipment and storage medium. Background technique [0002] At present, when diagnosing lesions that appear in human blood vessels, manual recognition is often used or a detector is used to perform global detection on the image to be recognized. However, there are often omissions in the manual recognition method, and the recognition results are mostly judged by the subjective experience of doctors, which is prone to judgment errors, resulting in low accuracy of the recognition results. , because the global detection mainly uses the trained model to complete the detection work, and the model trained by the neural network usually has errors, so the problem of missed detection of lesions is likely to occur, resulting in low accuracy of the recognition results. [0003] Therefore, it is necessary to provi...

AI Technical Summary

Problems solved by technology

However, there are often omissions in manual recognition methods, and the recognition results are mostly judged by the subjective experience of doctors, which is prone to judgment errors, resulting in low accuracy of the recognition results. , because the global detection mainly uses the trained model to complete the detection work, and the model t

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