Research on Abnormal Lung Sound Recognition and Diagnosis Based on Improved CNN and Transfomer

Lung auscultation is one of the effective means to diagnose lung diseases. This study aims at the lung sounds of patients, and studies the diagnosis and recognition method of abnormal lung sounds in patients with lung diseases by combining convolutional neural network CNN (Convolutional Neural Network) and Transformer. In this study, a realtime lung sound acquisition system was designed, and the lung sound information was uploaded to the PC terminal by using the wireless WIFI module. Under the guidance of professional physicians, the collected lung sounds of patients were labeled to establish a feature data set (normal, light, common, and heavy). Secondly, based on the Butterworth filter Gaussian filter and the nonnegative matrix factorization method, the lung sound denoising was performed and the MFCC feature information of the lung sound was extracted. Finally, the lung sound auxiliary diagnosis model was designed to analyze the characteristics of lung sounds, and the patient's condition was diagnosed and evaluated. This paper proposes an SCBAM-CNN-Transformer classification model. After optimizing CNN by improving the channel attention mechanism CBAM, the improved CNN model is combined with Transformer. It combines the advantages of CNN spatial feature representation and Transformer sequence coding, forming a strong global feature perception and local feature extraction ability. The algorithm proposed in this paper has good robustness and generalization ability, and the recognition accuracy is 94.8 %. The experimental results show that the proposed algorithm can more effectively identify abnormal lung sounds and determine the infection level than other deep learning models.