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COVID-19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成像的计算机化感染检测模型具有重要意义。开发了基于深度学习的新方法，用于 CT 辅助诊断 COVID-19。然而，目前大多数研究都是基于小规模的 COVID-19 CT 图像数据集，因为出于患者隐私原因，公开可用的数据集较少。因此，基于深度学习的检测模型的性能需要基于小数据集来提高。本文提出了一种堆叠式自动编码器检测器模型，大大提高了检测模型的准确率和召回率等性能。首先，构建四个自动编码器作为整个堆叠自动编码器检测器模型的前四层，以提取更好的 CT 图像特征。其次，将四个自动编码器级联在一起并连接到密集层和softmax分类器以构成模型。最后，通过叠加重建损失构建新的分类损失函数，以增强模型的检测精度。实验结果表明，我们的模型在小尺寸的 COVID-2019 CT 图像数据集上表现良好。我们的模型的平均准确率、精确率、召回率和 F1 得分率分别为 94.7%、96.54%、94.1% 和 94.8%。结果反映了我们的模型区分 COVID-19 图像的能力，这可能有助于放射科医生诊断疑似 COVID-19 患者。