使用改进的EfficientNet预测肺功能衰退

Yuxuan Tian
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引用次数: 1

摘要

肺纤维化(Pulmonary fibrosis, pf)是各种肺部疾病的常见结局,以肺组织瘢痕形成为主要表现,如果累及范围较广,可导致肺体积缩小,肺功能明显下降,严重影响患者的生活质量。利用CT扫描对高危人群进行检查是发现早期肺癌的有效手段。随着技术的发展,计算机辅助诊断在肿瘤诊断中发挥着越来越重要的作用。我们阐述了相关工作,并提出了一个改进的高效网。我们选择拉普拉斯对数似然作为实验指标。拉普拉斯对数似然值越高,模型的性能越好。我们可以看到,改进后的effennet模型的性能最好,其拉普拉斯对数似然值为-6.89,分别高于Resnet34、Resnet50和effentnet,分别为0.23、0.22和0.15。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lung Function Decline Predicting Using Improved EfficientNet
Pulmonary fibrosis (pf) is a common outcome of various lung diseases, with scarring of lung tissue as the main manifestation, and if the scope of involvement is extensive, it leads to a reduction in lung volume, a significant decrease in lung function, and a serious impact on the quality of life of patients. Using CT scanning to examine high-risk people is an effective means to find early lung cancer. With the development of technology, computer-aided diagnosis plays a very important role in the cancer diagnosis. We state the related work and proposed an improved EfficientNet. We choose Laplace Log Likelihood as our experiment metrics. The higher Laplace Log Likelihood is, the better performance the model will gain. We can see the result that our improved EfficienNet model owns the best performance with -6.89 Laplace Log Likelihood, which is 0.23, 0.22, 0.15 and higher than Resnet34, Resnet50 and EfficientNet respectively.
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