Image recognition method and device based on CT sequence, electronic equipment and medium

Abstract

The invention relates to a machine learning technology, and discloses an image recognition method based on a CT sequence, and the method comprises the steps: obtaining a tissue image sequence and a focus image sequence of a target pathological tissue; performing feature extraction on the tissue image sequence and the focus image sequence, and splicing the first feature image set and the second feature image set into a focus feature map; generating a prediction image label of the focus feature map; calculating a loss value between the prediction image label and a preset target pathological label, and updating the feature extraction model according to the loss value to obtain a target image recognition model; and performing image recognition on the to-be-recognized image sequence by using the target image recognition model to obtain a recognition result. The invention further provides an image recognition device based on the CT sequence and a computer readable storage medium. In addition, the invention also relates to a blockchain technology, and the to-be-identified image sequence can be stored in the blockchain node. The method and device can be applied to medical image recognition. According to the invention, the image identification efficiency and accuracy can be improved.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a CT sequence-based image recognition method, device, electronic equipment and computer-readable storage medium. Background technique [0002] In early 2020, the COVID-19 epidemic struck Wuhan. In February, Wuhan's medical and testing resources were extremely tight, and the sensitivity of nucleic acid testing was low. In order to "early detection, early isolation", the use of CT imaging to diagnose new coronary pneumonia was proposed. [0003] In the prior art, when performing new crown detection on a patient's CT image, it is necessary to use all sequences of the entire CT. For a CT sequence, there are thin slices and thick slices, 50 or 60 images for a thick slice, and 500 images for a thin slice. It takes 20 to 30 minutes to complete a diagnosis, and the diagnosis efficiency cannot meet the real-time nature of the actual situation. At the same time, a lot of...

AI Technical Summary

Problems solved by technology

For a CT sequence, there are thin slices and thick slices, 50 or 60 images for a thick slice, and 500 images for a thin slice. It takes 20 to 30 minutes to complete a diagnosis, and the diagnosis efficiency cannot meet the real-time performance of the actual situation.

At the same time, a lot of time also consumes a lot of energy for doctors, which is prone to missed diagnosis and misdiagnosis, resulting in low efficiency and accuracy of diagnosis

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