Cerebral infarction classification method based on deep learning

Abstract

The invention provides a cerebral infarction classification method based on deep learning. According to the method, the concept of deep learning is utilized, different tasks are calculated in parallelthrough the network, feature fusion is carried out on features extracted by the network in a subsequent feature layer, and therefore classification in the aspect of cerebral infarction is carried outthrough the fused features. According to the method, the information is fully utilized by utilizing the characteristic that the CT information and the MR information can complement each other, and meanwhile, the information is fused on a subsequent characteristic layer, so that redundant information is removed, computing resources of a computer are reduced, and the cerebral infarction detection precision is improved.

Description

technical field [0001] The present invention relates to the field of medical image recognition, in particular to a method for merging medical images using a deep learning method to classify cerebral infarction. Background technique [0002] Cerebral infarction, also known as ischemic stroke, is called stroke or stroke in traditional Chinese medicine. The disease is caused by a variety of causes of blood supply disturbance in the local brain tissue, leading to ischemic and hypoxic lesions and necrosis of the brain tissue, resulting in clinically corresponding neurological deficits. According to different pathogenesis, cerebral infarction is divided into main types such as cerebral thrombosis, cerebral embolism and lacunar infarction. Cerebral thrombosis is the most common type of cerebral infarction. [0003] In the medical field, researchers still use traditional algorithms to analyze and process images. Even if it can meet the precision requirements of medical processing,...

AI Technical Summary

Problems solved by technology

[0003] In the medical field, researchers still use traditional algorithms to analyze and process images. Even if it can meet the precision requirements of medical processing, it is very time-consuming and labor-intensive, requiring many experts to analyze and study images. Due to the diversity of stroke, evaluation Tissue damage necessitates accurate data models, and deep learning algorithms have shown promise in this area as they are able to capture complex image features in a large data environment while remaining robust to significant levels of noise

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