Advances in the Application of Deep Learning in the Diagnosis of Respiratory System Diseases

The incidence and mortality of respiratory diseases, including pneumonia, tuberculosis, lung cancer, etc., remain at high levels worldwide. Chest X-ray and computed tomography (CT) are the main screening and diagnostic tools for these diseases, but traditional imaging methods rely on the experience of doctors and have problems with subjectivity and consistency. In recent years, deep learning has shown great potential in the field of medical image analysis with its powerful automatic feature extraction and pattern recognition capabilities. Deep learning technology, especially convolutional neural networks (CNN), has developed from the early single disease classification to multi-task collaborative analysis, foci detection, risk assessment, etc. Especially in the imaging diagnosis of tuberculosis, pneumonia, and lung cancer, deep learning methods significantly improve the efficiency and accuracy of diagnosis. In addition, with the improvement of big data and computing power, the application of deep learning has been extended to foci detection, clinical auxiliary decision-making, and other fields. However, problems such as insufficient data standardisation, limited sample labelling, and model generalisation ability still restrict the clinical promotion of deep learning in the diagnosis of respiratory diseases. Future research should focus on multimodal image fusion, few-shot learning, and improved model interpretability, so as to promote the wide application of deep learning in respiratory diseases and improve the level of individualised diagnosis and treatment.