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Lung sound feature classification system and method based on deep learning and cloud platform

A technology of feature classification and deep learning, applied in neural learning methods, audio data clustering/classification, medical science, etc., can solve the problems of physical injury and insufficient accuracy of manual auscultation of lung sounds, so as to improve efficiency, facilitate learning and The effect of exercising auscultation ability and facilitating data sharing

Pending Publication Date: 2019-12-03
SOUTHWEAT UNIV OF SCI & TECH +1
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Problems solved by technology

[0004] Aiming at the above-mentioned deficiencies in the prior art, a lung sound feature classification system and method based on deep learning and cloud platform provided by the present invention solves the insufficient accuracy of manual auscultation of lung sounds, and the disadvantages of X-chest X-ray diagnosis method and CT diagnosis method. body problem

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  • Lung sound feature classification system and method based on deep learning and cloud platform
  • Lung sound feature classification system and method based on deep learning and cloud platform

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Embodiment Construction

[0049] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0050] Such as figure 1 As shown, the lung sound feature classification system based on deep learning and cloud platform includes a lung sound data acquisition module, a data preprocessing module, a spectrogram image data acquisition module, a feature training module, a feature classification module and a result feedback module;

[0051] The lung sound data acquisition module is used to collect lung sound audio data of know...

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Abstract

The invention discloses a lung sound feature classification system and method based on deep learning and a cloud platform. The system comprises a lung sound data acquisition module, a data preprocessing module, a spectrogram image data acquisition module, a feature training module, a feature classification module and a result feedback module. The lung sound can be obtained through the electronic stethoscope, the problems that lung sound feature obtaining is inaccurate due to manual auscultation, and an X-ray chest X-ray diagnostic method and a CT diagnostic method injure the body are solved, the features of the lung sound can be obtained in an unsubjective mode, lung sound feature data are provided for doctors, and the doctors can perform subsequent diagnosis conveniently.

Description

technical field [0001] The invention relates to the field of sound signal processing, in particular to a lung sound feature classification system and method based on deep learning and a cloud platform. Background technique [0002] Lung sounds are physiological signals produced during gas exchange in the respiratory system. Abnormal lung sounds are additional sounds that infiltrate into normal lung sounds during a respiratory cycle when the respiratory system develops lesions, such as wheezing, crackles, and crackles. Wheezing is common in obstructive pulmonary diseases (such as bronchial asthma, cystic fibrosis); crackles are common in the early stages of lobar pneumonia, pulmonary congestion, and pulmonary edema, and are also seen in diseases such as bronchioles and alveolitis; crackles It is common in pulmonary interstitial fibrosis, and patients with chronic bronchitis will also have this abnormal lung sound. The periodic oscillation of the tracheal wall is the source ...

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Application Information

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IPC IPC(8): G06F16/65G06F16/683G06F16/28G06N3/08A61B7/00
CPCG06F16/65G06F16/683G06F16/285G06N3/08A61B7/003
Inventor 刘满禄赵子豪张华宋宇张静周建
Owner SOUTHWEAT UNIV OF SCI & TECH
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