Medical image classification method and classification device thereof

Abstract

The invention discloses a medical image classification method and device, and relates to the technical field of digital image processing. The method comprises the steps: migrating a deep learning model trained in a natural image data set to the field of medical images through transfer learning, and obtaining a basic model of medical image classification; constructing a deep attention branch network based on class activation mapping on the basis of the basic model of medical image classification; constructing a loss weighting module based on a gray level co-occurrence matrix on the basis of the basic model of medical image classification; establishing fusion of a loss weighting module based on a gray level co-occurrence matrix and a deep attention branch network based on class activation mapping on the basis model of medical image classification to obtain a fused medical image classification model; training the fused medical image classification model to obtain a medical image automatic classification model; and automatically classifying the to-be-classified images through the medical image automatic classification model. The method can be used for accurate classification of medical images.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a medical image classification method and a classification device thereof. Background technique [0002] The classification of medical images is of great significance to the clinical diagnosis and evaluation of diseases. The traditional method of manually extracting features is cumbersome, the generalization is not strong, and the classification results for images with high similarity between normal and lesion regions are poor. Moreover, since the lesion area usually only occupies a small part of the image, this leads to the lack of ability of the deep convolutional neural network to focus on the meaningful lesion. In addition, there is usually a high degree of class imbalance in medical image datasets, and it is difficult to obtain a classification model with good sensitivity. Therefore, accurate classification of medical images remains a great challenge. Co...

AI Technical Summary

Problems solved by technology

The traditional method of manually extracting features is cumbersome, the generalization is not strong, and the classification results for images with high similarity between normal and lesion regions are poor

Moreover, since the lesion area usually only occupies a small part of the image, this leads to the lack of ability of the deep convolutional neural network to focus on the meaningful lesion

In addition, there is usually a high degree of class imbalance in medical image datasets, and it is difficult to obtain a classification model with good sensitivity

Therefore, the accurate classification of medical images remains a great challenge

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