Medical image classification and segmentation method and medical image classification and segmentation device

Abstract

The invention provides a medical image classification and segmentation method and a medical image classification and segmentation device. According to the invention, the problem in the prior art thatdue to the limited number of training data and the incomplete expression of the critical characteristics of medical images, the image classification and segmentation effect is poor can be solved. Themethod comprises the steps of carrying out data enhancement and pretreatment on a medical image, and constructing a classification data set and a segmentation data set; constructing a convolutional neural network model, extracting the characteristics of a natural image data set, and obtaining the activated characteristics of the convolutional neural network; based on the classification data set, training the convolutional neural network to extract the characteristics of the classification data set, so as to distinguish whether the medical image is a cancer image or a cancer-free image; and training the convolution neural network to extract the characteristics of the segmentation data set on the basis of the segmentation data set, so as to divide the canceration region of the medical image.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for classifying and segmenting medical images. Background technique [0002] Feature extraction plays an important role in the field of medical image analysis. Medical images have multiple modalities, such as MRI images, CT images, and digital pathology slides, which lead to images of multiple modalities from the same person under the same disease, with different textures, colors, and shapes. Therefore, feature design plays an important role in high-order medical image analysis tasks, such as classification tasks and segmentation tasks. The extraction of texture, color, and shape features of images based on manual methods is usually limited by professional knowledge, complex medical image features, and limited number of labeled medical images. Therefore, in medical image analysis, automatic feature extraction algorithms are of great significance for high-o...

AI Technical Summary

Problems solved by technology

[0005] 1. In the case of insufficient training data, the convolutional neural network does not fully express the characteristics of medical images

[0006] 2. Artificially designed features are difficult to fully express the clinical features of medical images, resulting in poor classification and segmentation of images

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