Lung disease recognition method and device based on CT (Computed Tomography) data, medium and electronic equipment

Abstract

The invention provides a lung disease recognition method and device based on CT data, a computer readable medium and electronic equipment, and relates to the technical field of image processing. The method comprises the following steps: acquiring a 3D tensor corresponding to lung CT data to be identified, and partitioning the 3D tensor to obtain k partitioned tensors; performing lung disease classification identification on the k block tensors to obtain k block classification results; and outputting an identification result corresponding to the to-be-identified lung CT data according to the k block classification results, and determining a focus block tensor corresponding to the to-be-identified lung CT data in the k block tensors when the identification result is an abnormal result. According to the method, the 3D tensor is partitioned, so that sampling of the 3D tensor can be avoided, and then missing of key focus information or introduction of repeated invalid noise is avoided; and meanwhile, the focus block tensor can be directly determined in the k block tensors through the k block classification result, so that the coarse positioning of the focus is realized based on the focus block tensor.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, and in particular to a method for identifying lung diseases based on CT data, a device for identifying lung diseases based on CT data, a computer-readable medium, and electronic equipment. Background technique [0002] CT (Computed Tomography), that is, electronic computerized tomography, its imaging principle is: use X-ray beams, γ-rays, ultrasound, etc. to scan a certain thickness of a specific part of the human body, and obtain medical images after computer processing. Compared with conventional imaging examination methods, CT has the advantages of being able to obtain real cross-sectional images, high density resolution, quantitative analysis, and convenient subsequent image processing. Therefore, it is more and more widely used in medical imaging detection. [0003] In recent years, the rapid development of computer-aided diagnosis technology has greatly facilitated the dia...

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Problems solved by technology

[0004] However, among the above three auxiliary analysis methods, the first method involves the sampling of fixed-size 3D tensors. Due to the lack of prior information, it is easy to cause the loss of key lesion information or the introduction of repetitive invalid noise; When the feature classifier is trained, it needs to provide accurate lesion area labels, so labeling is time-consuming and labor-intensive; the third method requires manual design of the feature space or feature space screening strategy, which is highly dependent on the quality of the input features, which can easily lead to model failure. overfitting

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