COVID-19 chest CT image recognition method and device and electronic equipment

Abstract

The invention relates to a COVID-19 chest CT image recognition method and device and electronic equipment. The method comprises the following steps: acquiring a COVID-19 chest CT image, constructing a new coronal pneumonia CT identification network according to the characteristics of the chest CT image, training the network to obtain a COVID-19 chest CT image identification model, and classifying the to-be-detected CT image by using the model. Redundant parameters are reduced by adopting a cavity convolution operator, a deep convolution operator and a point convolution operator; by adopting a parallel structure connection mode, multi-scale feature fusion is realized, and the model complexity is reduced; a down-sampling mode is adopted, maximum fuzzy pooling is used to reduce the sawtooth effect, and the translation invariance of the signal is kept; the channel shuffling operation is adopted, the parameter quantity and the calculation quantity are reduced, the classification accuracy is improved, a coordinate attention mechanism is introduced, space coordinate information and channel information are concerned, unimportant information is restrained, and the resource matching problem is solved.

Description

technical field [0001] This application relates to the technical field of image recognition, in particular to a COVID-19 chest CT image recognition method, device and electronic equipment. Background technique [0002] At present, the main medical methods for diagnosing pneumonia caused by the new coronavirus infection (COVID-19) include chest computed tomography (chest CT), chest X-ray image detection, and magnetic resonance imaging (MRI). For COVID-19 radiological examination, volumetric CT scan is the first choice, with a scan slice thickness of 5 mm (16 slices or more can be achieved), and the reconstruction is a thin slice of 1.0-1.5 mm. Based on thin-slice CT reconstruction, observation in the transverse, sagittal, and coronal planes is conducive to early detection of lesions, assessment of the nature and extent of lesions, and discovery of subtle changes that are difficult to observe with direct digital radiography (DR). However, for radiologists, reviewing lesion in...

AI Technical Summary

Problems solved by technology

However, the existing recognition methods have a complex network structure, a large amount of parameters, and a large amount of calculation. The unimportant information of the chest CT image has a negative impact on the classification results.

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