CN111920440A - Early lung cancer detection and classification integrated equipment and system based on deep learning - Google Patents
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Abstract
The invention relates to the field of image analysis, in particular to an early lung cancer detection and classification integrated system based on deep learning, which comprises a lung CT image acquisition module, a CT image preprocessing module, a lung image identification module, a lung nodule measurement module and a lung cancer screening module, wherein the lung image identification module realizes the detection and identification of lung nodules and lung nodule holes in lung images based on a DSSD _ Xconcentration model and intercepts lung nodule regions; the pulmonary nodule measurement module is used for measuring pulmonary nodules and pulmonary nodule hole sizes based on the length-width ratio of the connected component circumscribed rectangle and outputting measurement results; the lung cancer screening module is used for screening early lung cancer based on preset screening standards according to the measurement result of lung nodules. The method can automatically detect the type and probability of the early lung cancer, and can accurately identify the type, size, position and malignancy of the lung nodule.
Description
Technical Field
The invention relates to the field of image analysis, in particular to early lung cancer detection and classification integrated equipment and system based on deep learning.
Background
The lung cancer is the malignant tumor with the highest morbidity and mortality, the early symptoms are not obvious, and the lung cancer in the middle and late stages is extremely difficult to cure, so the early lung cancer detection is the main means for prolonging the life cycle of a patient and reducing the mortality. Early lung cancer is mostly represented by lung nodules, is large in number and small in size, low in contrast and easy to be confused with other tissues; at present, pulmonary nodule diagnosis is mainly carried out by adopting a pulmonary CT examination mode, doctors need to judge the malignancy degree of pulmonary nodules according to the size and the shape of the pulmonary nodules in CT images of patients, the number of CT images of a lung cancer patient is hundreds of levels, the workload of the doctors is heavy, the evaluation accuracy can be influenced by subjective factors such as doctor experience, fatigue degree, personal emotion and the like, meanwhile, the resource distribution of specialist doctors in each region is uneven, and missed diagnosis and misdiagnosis are easy to occur by adopting a traditional diagnosis and treatment mode.
In recent years, with the improvement of computer computing power and the stepwise increase of data volume, the deep learning technology is rapidly developed and is widely applied to the medical field, and scholars and research institutions at home and abroad make a great deal of research work on computer-aided systems for early lung cancer detection, the main technical steps include data preprocessing and lung nodule detection by using a convolution network, but the methods mainly focus on identifying lung nodule regions and non-lung nodule regions, on one hand, the categories of the lung nodules are not finely distinguished, on the other hand, the types and the probability of the lung nodule canceration are closely related to the types, the sizes, the shapes and other characteristics of the lung nodules, the traditional detection convolution network can only obtain the total probability of lung cancer of a patient, the specific types of the lung cancer cannot be distinguished, therefore, doctors are often required to evaluate the lung cancer, and integration of early lung cancer detection procedures is not realized.
Disclosure of Invention
In order to solve the problems, the invention provides early lung cancer detection and classification integrated equipment and system based on deep learning, which can automatically detect the type and probability of early lung cancer and accurately identify the type, size, position and malignancy of lung nodules.
In order to achieve the purpose, the invention adopts the technical scheme that:
an early lung cancer detection and classification integrated system based on deep learning comprises:
the lung CT image acquisition module is used for acquiring a lung CT image through a high-resolution computed tomography technology;
the CT image preprocessing module is used for positioning a target region based on a Faster R-CNN model, filtering parts which are not obvious in contrast, such as ribs, muscles and the like, intercepting the region of interest, calculating and improving the significant value of lung tissues of the region of interest by combining the contrast of the region of interest, and intercepting a lung image through the obtained significant value image;
the lung image identification module is used for realizing the detection and identification of lung nodules and lung nodule holes in the lung image based on the DSSD _ Xcenter model and intercepting lung nodule areas;
the pulmonary nodule measurement module is used for measuring pulmonary nodules and pulmonary nodule hole sizes based on the length-width ratio of the connected component circumscribed rectangle and outputting measurement results;
and the lung cancer screening module is used for realizing screening of early lung cancer based on a preset screening standard according to the measurement result of the lung nodules.
Further, the DSSD _ Xception model adopts a DSSD target detection algorithm and is obtained by training based on preset lung images with lung nodules and/or lung nodule holes.
Further, the pulmonary nodule measurement module firstly realizes three-dimensional reconstruction of a pulmonary nodule region image based on MATLAB, and then measures the sizes of pulmonary nodules and pulmonary nodule holes in the three-dimensional image based on the aspect ratio of connected component circumscribed rectangles.
Further, the lung cancer screening module identifies the type and the deterioration degree of the lung nodules based on a fuzzy neural network algorithm according to the type, the position parameter, the quantity parameter and the size measurement result of the lung nodules based on preset screening standards.
Further, still include:
and the detection guiding module is used for calibrating the image acquisition position of the CT image acquisition module according to the positioning data and the body type data of the patient so as to guide the CT image acquisition module to reach the target position to finish the acquisition of the lung CT image.
Furthermore, the detection guiding module acquires positioning data of the patient through a position frame marked on the ground, the image acquisition module acquires a body shape image of the patient, binarization of the body shape image is achieved based on a maximum entropy threshold method, small-area noise is removed based on morphological region characteristics of connected components, the position of the upper half of the body of the patient is identified, and then calibration of image acquisition coordinates of the CT image acquisition module is achieved based on the position reference standard of the upper half of the lung, the identification result of the position of the upper half of the body and the positioning data of the patient.
Further, each pulmonary nodule region intercepted by the pulmonary image identification module carries the position parameter of the pulmonary nodule region in the pulmonary tissue.
The invention also provides early lung cancer detection and classification integrated equipment based on deep learning, and the detection and classification integrated system is adopted to realize the detection and classification of early lung cancer.
The invention has the following beneficial effects:
1) the method can automatically detect the type and probability of the early lung cancer, and can accurately identify the type, size, position and malignancy of the lung nodule.
2) The quality of the CT image can be improved, and a foundation is provided for the subsequent identification work of the type, size, position and malignancy degree of the lung nodule.
Drawings
Fig. 1 is a system block diagram of an early lung cancer detection and classification integrated system based on deep learning according to an embodiment of the present invention.
Fig. 2 is a system block diagram of an early lung cancer detection and classification integrated device based on deep learning according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described in detail below with reference to examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, an early lung cancer detection and classification integrated system based on deep learning includes:
the detection guiding module is used for calibrating the image acquisition position of the CT image acquisition module according to the positioning data and the body type data of the patient so as to guide the CT image acquisition module to reach a target position to finish acquisition of the lung CT image;
the lung CT image acquisition module is used for acquiring a lung CT image through a high-resolution computed tomography technology;
the CT image preprocessing module is used for positioning a target region based on a Faster R-CNN model, filtering parts which are not obvious in contrast, such as ribs, muscles and the like, intercepting the region of interest, calculating and improving the significant value of lung tissues of the region of interest by combining the contrast of the region of interest, and intercepting a lung image through the obtained significant value image;
the lung image identification module is used for realizing the detection and identification of lung nodules and lung nodule holes in the lung image based on the DSSD _ Xcenter model and intercepting lung nodule areas; each pulmonary nodule region intercepted by the pulmonary image identification module carries position parameters of the pulmonary nodule region in the pulmonary tissue;
the pulmonary nodule measurement module is used for measuring pulmonary nodules and pulmonary nodule hole sizes based on the length-width ratio of the connected component circumscribed rectangle and outputting measurement results;
and the lung cancer screening module is used for realizing screening of early lung cancer based on a preset screening standard according to the measurement result of the lung nodules.
In this embodiment, the DSSD _ Xception model is obtained by training based on a preset lung image with lung nodules and/or lung nodule holes by using a DSSD target detection algorithm.
In this embodiment, the pulmonary nodule measurement module first implements three-dimensional reconstruction of a pulmonary nodule region image based on MATLAB, and then measures the sizes of pulmonary nodules and pulmonary nodule holes in the three-dimensional image based on the aspect ratio of the connected component circumscribed rectangle.
In this embodiment, the lung cancer screening module identifies the type and the degree of deterioration of the lung nodules based on a fuzzy neural network algorithm based on a preset screening standard according to the type, the position parameter, the number parameter, and the size measurement result of the lung nodules.
In this embodiment, the detection guidance module acquires positioning data of a patient through a position frame marked on the ground, acquires a body shape image of the patient through the image acquisition module, realizes binarization of the body shape image based on a maximum entropy threshold method, removes small-area noise based on morphological region characteristics of connected components, completes identification of the position of the upper half of the body of the patient, and then realizes calibration of image acquisition coordinates of the CT image acquisition module based on a position reference standard of the upper half of the lung, an identification result of the position of the upper half of the body, and the positioning data of the patient.
Example 2
As shown in fig. 2, an early lung cancer detection and classification integrated device based on deep learning includes a detection guide module, a lung CT image acquisition module, and a lung CT image post-processing system loaded in a PC, wherein the detection guide module is configured to calibrate an image acquisition position of the CT image acquisition module according to positioning data and body type data of a patient, so as to guide the CT image acquisition module to reach a target position to complete acquisition of a lung CT image; the lung CT image acquisition module is used for acquiring a lung CT image through a high-resolution computed tomography technology; the lung CT image post-processing system comprises:
the CT image preprocessing module is used for positioning a target region based on a Faster R-CNN model, filtering parts which are not obvious in contrast, such as ribs, muscles and the like, intercepting the region of interest, calculating and improving the significant value of lung tissues of the region of interest by combining the contrast of the region of interest, and intercepting a lung image through the obtained significant value image;
the lung image identification module is used for realizing the detection and identification of lung nodules and lung nodule holes in the lung image based on the DSSD _ Xcenter model and intercepting lung nodule areas; each pulmonary nodule region intercepted by the pulmonary image identification module carries position parameters of the pulmonary nodule region in the pulmonary tissue;
the pulmonary nodule measurement module is used for measuring pulmonary nodules and pulmonary nodule hole sizes based on the length-width ratio of the connected component circumscribed rectangle and outputting measurement results;
and the lung cancer screening module is used for realizing screening of early lung cancer based on a preset screening standard according to the measurement result of the lung nodules.
In this embodiment, the DSSD _ Xception model is obtained by training based on a preset lung image with lung nodules and/or lung nodule holes by using a DSSD target detection algorithm.
In this embodiment, the pulmonary nodule measurement module first implements three-dimensional reconstruction of a pulmonary nodule region image based on MATLAB, and then measures the sizes of pulmonary nodules and pulmonary nodule holes in the three-dimensional image based on the aspect ratio of the connected component circumscribed rectangle.
In this embodiment, the lung cancer screening module identifies the type and the degree of deterioration of the lung nodules based on a fuzzy neural network algorithm based on a preset screening standard according to the type, the position parameter, the number parameter, and the size measurement result of the lung nodules.
In this embodiment, the detection guidance module acquires positioning data of a patient through a position frame marked on the ground, acquires a body shape image of the patient through the image acquisition module, realizes binarization of the body shape image based on a maximum entropy threshold method, removes small-area noise based on morphological region characteristics of connected components, completes identification of the position of the upper half of the body of the patient, and then realizes calibration of image acquisition coordinates of the CT image acquisition module based on a position reference standard of the upper half of the lung, an identification result of the position of the upper half of the body, and the positioning data of the patient.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.
Claims (8)
1. An early lung cancer detection and classification integrated system based on deep learning is characterized by comprising:
the lung CT image acquisition module is used for acquiring a lung CT image through a high-resolution computed tomography technology;
the CT image preprocessing module is used for positioning a target region based on a Faster R-CNN model, intercepting the region of interest when the contrast is not obvious, calculating and improving the significant value of the lung tissue of the region of interest by combining the contrast of the region of interest, and intercepting the lung image through the obtained significant value image;
the lung image identification module is used for realizing the detection and identification of lung nodules and lung nodule holes in the lung image based on the DSSD _ Xcenter model and intercepting lung nodule areas;
the pulmonary nodule measurement module is used for measuring pulmonary nodules and pulmonary nodule hole sizes based on the length-width ratio of the connected component circumscribed rectangle and outputting measurement results;
and the lung cancer screening module is used for realizing screening of early lung cancer based on a preset screening standard according to the measurement result of the lung nodules.
2. The deep learning-based early lung cancer detection and classification integrated system of claim 1, wherein the DSSD _ Xception model is trained based on preset lung images with lung nodules and/or lung nodule holes by using a DSSD target detection algorithm.
3. The deep learning-based early lung cancer detection and classification integrated system as claimed in claim 1, wherein the pulmonary nodule measurement module first performs three-dimensional reconstruction of an image of a pulmonary nodule region based on MATLAB, and then performs measurement of sizes of pulmonary nodules and pulmonary nodule holes in the three-dimensional image based on an aspect ratio of a connected component bounding rectangle.
4. The deep learning-based early lung cancer detection and classification integrated system as claimed in claim 1, wherein the lung cancer screening module is based on a fuzzy neural network algorithm to identify the type and the deterioration degree of lung nodules according to the type, the location parameter, the number parameter and the size measurement result of the lung nodules based on preset screening criteria.
5. The deep learning-based early lung cancer detection and classification integrated system according to claim 1, further comprising:
and the detection guiding module is used for calibrating the image acquisition position of the CT image acquisition module according to the positioning data and the body type data of the patient so as to guide the CT image acquisition module to reach the target position to finish the acquisition of the lung CT image.
6. The system as claimed in claim 5, wherein the detection guidance module obtains the positioning data of the patient through a position frame marked on the ground, the image acquisition module acquires the body shape image of the patient, the binarization of the body shape image is realized based on a maximum entropy threshold method, the small area noise is removed based on the morphological region characteristics of the connected components, the identification of the position of the upper half of the body of the patient is completed, and then the calibration of the image acquisition coordinates of the CT image acquisition module is realized based on the position reference standard of the upper half of the lung, the identification result of the position of the upper half of the body and the positioning data of the patient.
7. The deep learning-based early lung cancer detection and classification integrated system as claimed in claim 1, wherein each lung nodule region intercepted by the lung image recognition module carries the location parameter of the lung tissue in which the lung nodule region is located.
8. The utility model provides an early lung cancer detects categorised integration equipment based on deep learning which characterized in that: the detection and classification integration system of any one of claims 1-7 is used for early stage lung cancer detection and classification.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112529894A (en) * | 2020-12-22 | 2021-03-19 | 徐州医科大学 | Thyroid nodule diagnosis method based on deep learning network |
| CN113591791A (en) * | 2021-08-16 | 2021-11-02 | 郑州大学 | Lung cancer automatic identification system based on self-learning artificial intelligence |
| CN117532216A (en) * | 2023-12-21 | 2024-02-09 | 重庆工业设备安装集团有限公司 | Steel structure welding robot |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112529894A (en) * | 2020-12-22 | 2021-03-19 | 徐州医科大学 | Thyroid nodule diagnosis method based on deep learning network |
| CN112529894B (en) * | 2020-12-22 | 2022-02-15 | 徐州医科大学 | Thyroid nodule diagnosis method based on deep learning network |
| CN113591791A (en) * | 2021-08-16 | 2021-11-02 | 郑州大学 | Lung cancer automatic identification system based on self-learning artificial intelligence |
| CN117532216A (en) * | 2023-12-21 | 2024-02-09 | 重庆工业设备安装集团有限公司 | Steel structure welding robot |
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