Lung pathological sound automatic analysis method based on multi-task classification

Abstract

The invention discloses a lung pathological sound automatic analysis method based on multi-task classification, and relates to the technical field of lung pathology analysis. The method comprises the following steps: inputting extracted audio features into a multi-task classification model of a convolutional neural network MobileNetV2, wherein the multi-task classification model of the convolutional neural network comprises the steps of outputting a lung pathological sound recognition task and outputting a lung illness prediction task. According to the method, the training data volume can be implicitly increased by adopting a multi-task learning method, the generalization performance of the model is improved through domain knowledge of multiple pieces of label information of the same data, so that the prediction accuracy of the multi-task classification model of the convolutional neural network MobileNetV2 is improved, and in addition, the lightweight multi-task classification model of the convolutional neural network MobileNetV2 is adopted, so that the number of parameters is small, the requirements for the computing power and the memory size of training equipment are small, and the prediction classification task can be completed on mobile or embedded equipment.

Description

technical field [0001] The invention relates to the technical field of pulmonary pathological analysis, in particular to an automatic analysis method for pulmonary pathological sounds based on multi-task classification. Background technique [0002] Studies have shown that deterioration of the status of subjects with pulmonary conditions (eg, asthma, chronic obstructive pulmonary disease (COPD), emphysema, cystic fibrosis, etc.) is characterized by a combination of aspects. Breathing defects cause dyspnea (shortness of breath) and coughing. In fact, often increased dyspnea and increased sputum purulence and / or volume (which leads to increased coughing) are considered the most distinctive or cardinal symptoms of exacerbation of pulmonary disease. [0003] Lung sound signal is a kind of physiological sound signal generated by the human respiratory system and the outside world during ventilation. main method. However, stethoscope-based diagnosis has some shortcomings, such a...

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Problems solved by technology

[0007] Aiming at the problems in related technologies, the present invention proposes an automatic analysis method for lung pathological sounds based on multi-task classification, to overcome the small amount of data in the existing lung sound data set and the large noise interference in lung sounds, so for a single For the lung sound recognition task, it is difficult to distinguish relevant and irrelevant features, and the generalization ability of the model is weak, resulting in poor classification performance, and the network model used in the prior art is complex and has many parameters, which affects the computing power and memory size of the training equipment. The demand is relatively large, and technical issues that need to be run on a large server

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