Classification method for chest CT (Computed Tomography) images based on deep learning

Abstract

The invention relates to a classification method for chest CT (Computed Tomography) images based on deep learning. In the method, characteristics of the chest CT images are extracted and analyzed by use of a relatively mature deep learning method at present, and a model structure is trained by use of an existing image data set to implement quantitative classification of multiple indexes affectingdisease judgment. Therefore, a quantified physiological index classification result is provided for a diagnosis doctor, and the work load and the analytical judgment difficulty of an occupational physician are reduced.

Description

technical field [0001] The present invention relates to the intersection field of deep learning and medical image processing in the field of artificial intelligence, and specifically relates to a method for classifying chest CT images based on deep learning. Background technique [0002] With the rapid development of society and the continuous improvement of the quality of life, people pay more and more attention to health. As the diagnosis basis of clinical treatment, the accurate identification of medical imaging technology is becoming more and more important. Pulmonary disease is a common disease, including atelectasis, effusion, infiltrates, lung masses, nodules, pneumonia, etc. The general diagnosis method is to judge whether there is a certain disease based on the chest CT image information. In general, professional doctors with rich clinical experience can make accurate judgments based on their own experience. However, when the differences between different genders a...

AI Technical Summary

Problems solved by technology

In general, professional doctors with rich clinical experience can make accurate judgments based on their own experience. However, when the differences between different genders and ages are particularly large, the increasing workload and the difficulty of identification gradually increase, especially in the comparison of medical resources. In today's shortage, a machine-assisted diagnostic imaging technology that replaces traditional diagnostic identification methods will undoubtedly speed up the diagnosis and treatment process of patients

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