Li Daqiu, Fu Zhangjie, Xu Jun
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044 China.
Peng Cheng Laboratory, Shenzhen, 518000 China.
Appl Intell (Dordr). 2021;51(5):2805-2817. doi: 10.1007/s10489-020-02002-w. Epub 2020 Nov 9.
With the outbreak of COVID-19, medical imaging such as computed tomography (CT) based diagnosis is proved to be an effective way to fight against the rapid spread of the virus. Therefore, it is important to study computerized models for infectious detection based on CT imaging. New deep learning-based approaches are developed for CT assisted diagnosis of COVID-19. However, most of the current studies are based on a small size dataset of COVID-19 CT images as there are less publicly available datasets for patient privacy reasons. As a result, the performance of deep learning-based detection models needs to be improved based on a small size dataset. In this paper, a stacked autoencoder detector model is proposed to greatly improve the performance of the detection models such as precision rate and recall rate. Firstly, four autoencoders are constructed as the first four layers of the whole stacked autoencoder detector model being developed to extract better features of CT images. Secondly, the four autoencoders are cascaded together and connected to the dense layer and the softmax classifier to constitute the model. Finally, a new classification loss function is constructed by superimposing reconstruction loss to enhance the detection accuracy of the model. The experiment results show that our model is performed well on a small size COVID-2019 CT image dataset. Our model achieves the average accuracy, precision, recall, and F1-score rate of 94.7%, 96.54%, 94.1%, and 94.8%, respectively. The results reflect the ability of our model in discriminating COVID-19 images which might help radiologists in the diagnosis of suspected COVID-19 patients.
随着新冠疫情的爆发，基于计算机断层扫描（CT）的医学成像诊断被证明是对抗病毒快速传播的有效方法。因此，研究基于CT成像的传染病检测计算机模型具有重要意义。针对新冠病毒肺炎（COVID-19）的CT辅助诊断，人们开发了新的基于深度学习的方法。然而，由于患者隐私原因，公开可用的数据集较少，目前大多数研究都是基于小规模的COVID-19 CT图像数据集。因此，基于小规模数据集的深度学习检测模型的性能有待提高。本文提出了一种堆叠自动编码器检测模型，以大幅提高检测模型的性能，如精确率和召回率。首先，构建四个自动编码器作为整个堆叠自动编码器检测模型的前四层，以提取更好的CT图像特征。其次，将这四个自动编码器级联在一起，并连接到全连接层和softmax分类器以构成模型。最后，通过叠加重构损失构建一种新的分类损失函数，以提高模型的检测精度。实验结果表明，我们的模型在小规模的COVID-19 CT图像数据集上表现良好。我们的模型平均准确率、精确率、召回率和F1分数率分别达到了94.7%、96.54%、94.1%和94.8%。这些结果反映了我们的模型在区分COVID-19图像方面的能力，这可能有助于放射科医生诊断疑似COVID-19患者。
