Medical image classification method and system and storage medium

Abstract

The invention discloses a medical image classification method and system and a storage medium, which can be applied to the technical field of image classification. The method comprises the following steps: respectively segmenting sequence images, and constructing a target area three-dimensional image corresponding to a target area image obtained by segmentation; inputting the three-dimensional image of the target area into a full convolutional neural network model to obtain an image disease probability graph; inputting the image disease probability graph into a Bayesian neural network model to obtain a classification result and uncertainty corresponding to the medical image; generating a fitting curve of credibility and uncertainty intervals according to the classification results and uncertainty corresponding to all the medical images; when the fitting curve meets a preset requirement, determining an uncertain target interval; and determining that the uncertainty corresponding to the medical image belongs to an uncertainty target interval, and taking a classification result corresponding to the medical image as a target classification result. According to the invention, the classification result of the medical image given by the current classification algorithm can better conform to the actual situation.

Description

technical field [0001] The invention relates to the technical field of image classification, in particular to a medical image classification method, system and storage medium. Background technique [0002] In the process of medical image classification, classification algorithms are usually used to improve the processing speed of medical image classification. In related technologies, before the classification algorithm is used, training data is used to train the classification algorithm, and then the trained algorithm is applied to an actual image classification process. However, there may be a large difference between the images involved in the actual classification process and the images involved in the training process, so that the classification results obtained after the trained classification algorithm classifies the actual images have a large discrepancy with the actual classification effect. Differences, thus affecting the accuracy of medical diagnosis based on clas...

AI Technical Summary

Problems solved by technology

However, there may be a large difference between the images involved in the actual classification process and the images involved in the training process, so that the classification results obtained after the trained classification algorithm classifies the actual images have a large discrepancy with the actual classification effect. difference, thus affecting the accuracy of medical diagnosis based on classification results

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