Dalian University of Technology, Dalian 116024, China.
The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China.
J Healthc Eng. 2022 Mar 26;2022:3845008. doi: 10.1155/2022/3845008. eCollection 2022.
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.
作为一种致命的肺部疾病，肺纤维化会对肺部造成不可逆转的损伤，影响正常的肺功能，最终导致死亡。目前，这种疾病的发病机制尚不完全清楚，也没有根治方法。治疗这种疾病的主要目的是减缓肺纤维化的恶化。对于这种疾病，如果能早期发现，就可以尽早进行治疗，延长患者的生命。临床上，肺纤维化的诊断依赖于相关的影像学检查、肺活检、肺功能检查等。X 射线等影像学数据是临床医学中的一种常见检查手段，对肺纤维化的预测也起着重要作用。通过 X 射线，放射科医生可以清楚地看到相关的肺部病变，从而做出相关诊断。本文基于常见的医学影像数据，设计相关模型来完成肺纤维化的预测。本文设计的模型主要分为两部分：第一部分，本文使用神经网络完成肺器官的分割；第二部分，设计图像分类的神经网络，完成从肺部图像到疾病预测的过程。在这两部分的设计中，本文在之前研究方法的基础上进行了改进。通过设计具有更高性能的神经网络，在关键指标上取得了更优化的结果，可应用于肺纤维化预测的真实场景。
