Integrated ResNet-NRC method for dividing sample space based on lung tumor image

A tumor image and sample space technology, which is applied in the field of medical image recognition, can solve the problems of high accuracy, large difference, and the specificity and sensitivity of medical image recognition cannot meet the standards, and achieve good robustness and generalization. The effect of excellent ability, classification accuracy, and classification performance

Pending Publication Date: 2020-08-25
BEIFANG UNIV OF NATITIES
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Problems solved by technology

[0004] However, the existing ensemble learning methods cannot meet the conditions of high accuracy and large differences for the base classifier, and their specificity and sensitivity for medical image recognition cannot meet the standards.

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  • Integrated ResNet-NRC method for dividing sample space based on lung tumor image
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  • Integrated ResNet-NRC method for dividing sample space based on lung tumor image

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Embodiment Construction

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0034] The embodiment of the present invention discloses an integrated ResNet-NRC method based on lung tumor image division sample space, establishes multiple homogeneous and different base classifiers to solve the same problem, and then combines the predictions of all base classifiers As a result, the final prediction result of ensemble learning can be obtained through a certain combination of strategies. In ensemble learning, it is generally believed that in...

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Abstract

The invention discloses an integrated ResNet-NRC method for dividing a sample space based on a lung tumor image, and the method comprises the following steps: collecting medical image information of the same lung tumor in three dimensions, i.e., a three-mode data set CT, a PET and a PET / CT; dividing sample feature spaces of three modes according to the three-mode data set CT, PET and PET / CT; constructing residual neural network models of the three modes according to the sample feature spaces of the three modes, namely a base classifier; and combining the three base classifiers by adopting a relative majority voting method to form a final classification identification result. According to the method, the classification accuracy is excellent, the conditions of high accuracy and large difference of a base classifier are met, the optimization problem of high-dimensional data can be effectively solved, the specificity, sensitivity and other evaluation indexes are high, and the method has good robustness and generalization ability.

Description

technical field [0001] The present invention relates to the technical field of medical image recognition, and more specifically relates to an integrated ResNet-NRC method based on lung tumor image division sample space. Background technique [0002] Medical imaging methods are widely used in the diagnosis of lung tumors, including X-ray imaging, computerized tomography (CT), positron emission computed tomography (PET) and magnetic resonance imaging (Magnetic Resonance Imaging, MRI), etc., PET is mainly based on tracers to selectively reflect the metabolism of tissues and organs. It reflects the physiological, pathological, biochemical and metabolic changes of human tissues at the molecular level, and is suitable for the study of human physiological functions. 18F-FDG PET / CT combines the advantages of PET and CT, and integrates anatomical images (CT) and functional metabolic images (PET) on the same machine, which has both fine anatomical structures and rich physiological, b...

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Application Information

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IPC IPC(8): G06K9/62G06K9/32G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06V10/25G06V10/44G06N3/045G06F18/24
Inventor 周涛陆惠玲霍兵强贺钧丁红胜张飞飞董雅丽
Owner BEIFANG UNIV OF NATITIES
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