Abstract
As a fatal lung disease, pulmonary fibrosis can cause irreversible damage to the lung, affect normal lung function, and eventually lead to death. At present, the pathogenesis of this kind of disease is not completely clear, and there is no radical cure. The main purpose of the treatment of this disease is to slow down the deterioration of pulmonary fibrosis. For this kind of disease, if it can be found early, it can be treated as soon as possible and the life of patients will be prolonged. Clinically, the diagnosis of pulmonary fibrosis depends on the relevant imaging examination, lung biopsy, lung function examination, and so on. Imaging data such as X-rays is a common examination means in clinical medicine and also plays an important role in the prediction of pulmonary fibrosis. Through X-ray, radiologists can clearly see the relevant lung lesions so as to make the relevant diagnosis. Based on the common medical image data, this paper designs related models to complete the prediction of pulmonary fibrosis. The model designed in this paper is mainly divided into two parts: first, this paper uses a neural network to complete the segmentation of lung organs; second, the neural network of image classification is designed to complete the process from lung image to disease prediction. In the design of these two parts, this paper improves on the basis of previous research methods. Through the design of a neural network with higher performance, more optimized results are achieved on the key indicators which can be applied to the real scene of pulmonary fibrosis prediction.