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Lung nodule detection model construction method

A detection model and construction method technology, applied in the field of pulmonary nodule detection model construction, can solve the problems of low detection method efficiency, inconvenient operation, and inability to detect large-scale samples, and achieve great practical value, increase accuracy, and reduce calculations volume effect

Inactive Publication Date: 2018-08-28
HENAN UNIVERSITY OF TECHNOLOGY +1
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings of existing detection methods such as low efficiency, inconvenient operation, and failure to apply to large-scale sample detection, and provide a lung nodule detection method with high accuracy, simple calculation, fast speed, and high degree of automation

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  • Lung nodule detection model construction method

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Embodiment Construction

[0038] The specific implementation of the present invention will be described below in conjunction with the accompanying drawings and examples, but the following examples are only used to describe the present invention in detail, and do not limit the scope of the present invention in any way.

[0039] The programs involved or relied on in the following embodiments are conventional programs or simple programs in the technical field. Unless otherwise specified, those skilled in the art can make conventional selections or adaptive adjustments according to specific application scenarios.

[0040] Such as figure 1 Shown is a schematic diagram of the construction steps of the detection model in this embodiment. It can be seen from the figure that firstly, the pulmonary nodule data set is made and preprocessed; then, the network structure is designed and the parameters of the network are adjusted; then, the network is trained to obtain a network model capable of detecting pulmonary n...

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Abstract

The invention discloses a lung nodule detection model construction method, and aims to solve the technical problem that the existing detection method is low in efficiency, inconvenient to operate andincapable of being applied to large-scale samples. The method comprises the following steps: (1) screening out a lung nodule training sample and carrying out the data enhancement; (2) constructing a Caffe depth learning framework, wherein the configuration of the model is completed by utilizing a Faster R-CNN network, and a VGG-16 network structure is introduced; (3) performing the applicable lungnodule image improvement on the detection model; (4) adjusting the network parameters to enable the model to be converged; (5) optimizing by utilizing a loss function; (6) training the designed network, so as to obtain an image recognition network with the function of detecting lung nodules. The method has the advantages of being high in detection speed and high in precision, being applied to large-scale samples, being capable of self-updating iteration, and being high in intelligent degree.

Description

technical field [0001] The invention belongs to the technical field of target detection, in particular to a method for constructing a pulmonary nodule detection model. Background technique [0002] Lung cancer ranks first in cancer mortality, and its five-year survival rate is only 17.4%. Early detection, early diagnosis, and early treatment are the key to saving the lives of lung cancer patients. The early clinical manifestation of lung cancer is malignant pulmonary nodules. Therefore, to detect lung cancer early, it is necessary to detect whether there are nodules in the lungs, and then judge whether the nodules are benign or malignant. The introduction of computed tomography (CT) in recent years has made the early screening of lung cancer possible. But it is followed by a large number of chest CT images, which seriously increases the workload of reviewers. How to quickly and automatically detect the exact location of pulmonary nodules in hundreds of two-dimensional CT ...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/20081G06T2207/20084G06T2207/30064
Inventor 张庆辉万晨霞韩伟良
Owner HENAN UNIVERSITY OF TECHNOLOGY
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