Intracranial hemorrhage sub-type classification algorithm applied to CT image based on bilinear pooling

Abstract

The invention discloses an intracranial hemorrhage sub-type classification algorithm applied to a CT image based on bilinear pooling, the algorithm innovatively solves the problem of intracranial hemorrhage sub-type classification from the perspective of fine-grained classification, a compact bilinear pooling network architecture is used in a CNN part, the feature expression ability of a neural network is improved, and the classification performance is improved. Bilinear pooling has been proved to be an effective method for solving the fine-grained classification problem, and comprises the steps: modeling high-order statistical information; regarding the features of CNNs from two different sources or homologous CNNs as two different features; carrying out outer product calculation on the two features, carrying out feature fusion through pooling operation to capture the relationship between different features, and obtaining stronger global feature representation. According to the method, interactive modeling is carried out on local paired features by using an image translation invariant method.

Description

technical field [0001] The invention belongs to the field of intelligent medical image processing, and relates to a bilinear pooling-based classification algorithm applied to subtypes of intracranial hemorrhage. Background technique [0002] Intracranial hemorrhage (ICH) is a bleeding disorder that occurs in the ventricle or brain tissue. Intracranial hemorrhage includes: intraventricular hemorrhage (IVH), parenchymal hemorrhage (IPH), subarachnoid hemorrhage (SAH), epidural hemorrhage (EDH) and subdural hematoma (SDH). The study of subtype classification of intracranial hemorrhage is to judge whether there is intracranial hemorrhage in CT images and to classify its five subtypes (IPH, IVH, EDH, SDH, SAH). Diagnosing intracranial hemorrhage is a major challenge in medicine. Determining the site and type of intracranial hemorrhage is key to treating the patient. The current method of diagnosing intracranial hemorrhage is a CT scan of the brain. Because tissues such as bra...

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Problems solved by technology

[0004] In order to solve the problem that the subtype classification of intracranial hemorrhage is time-consuming and highly dependent on the professionalism of doctors, the present invention provides a subtype classification algorithm of intracranial hemorrhage based on bilinear pooling applied to CT images

The present invention proposes a new CNN-RNN network architecture for subtype classification of ICH. In the CNN module, starting from the characteristics of intracranial CT images, this problem is viewed from a fine-grained perspective, which solves the problems of ResNet and DenseNet networks. Defects on intracranial CT images

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