An automatic analysis method for lung pathological sounds based on multi-task classification

Abstract

The invention discloses a method for automatic analysis of lung pathological sounds based on multi-task classification, relates to the technical field of lung pathology analysis, and comprises the following steps: inputting the extracted audio features into a multi-task classification model of a convolutional neural network MobileNetV2, The multi-task classification model of the convolutional neural network MobileNetV2 includes an output for a lung pathological sound recognition task and an output for a lung disease prediction task. The present invention uses the multi-task learning method to implicitly increase the amount of training data, and improves the generalization performance of the model through the domain knowledge of multiple label information of the same data, thereby improving the prediction accuracy of the multi-task classification model of the convolutional neural network MobileNetV2 In addition, the multi-task classification model using the lightweight convolutional neural network MobileNetV2 has fewer parameters and requires less computing power and memory size for training equipment, so that the prediction and classification tasks can be completed on mobile or embedded devices.

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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