The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient’s clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement.

新型冠状病毒 (COVID-19) 肺炎已成为世界各国面临的严重健康挑战。许多放射学发现表明，X 射线和 CT 成像扫描是评估 COVID-19 早期疾病严重程度的有效解决方案。许多人工智能 (AI) 辅助诊断工作被迅速提出，以专注于解决这一分类问题并确定患者是否感染了 COVID-19。这些工作中的大多数都设计了网络并应用单个 CT 图像进行分类；然而，这种方法忽略了先前的信息，例如患者的临床症状。其次，对临床严重程度做出更具体的诊断，如轻微或严重，值得关注，有利于确定更好的后续治疗方案。在本文中，我们提出了一个名为 FaNet 的基于深度学习 (DL) 的双任务网络，该网络可以基于 3D CT 成像和临床症状的组合对 COVID-19 进行快速诊断和严重程度评估。通常，3D CT 图像序列比单个 CT 图像提供更多的空间信息。此外，可以将临床症状作为先验信息，提高评估准确性；放射科医生通常可以快速轻松地获得这些症状。因此，我们设计了一个同时考虑 CT 图像信息和现有临床症状信息的网络，并对 416 名患者数据进行了实验，其中包括 207 例胸部 CT 正常病例和 209 例 COVID-19 确诊病例。实验结果证明了附加症状先验信息以及网络架构设计的有效性。所提出的 FaNet 在诊断评估上的准确率达到了 98.28%，在测试数据集的严重性评估上达到了 94.83%。未来，我们将收集更多的 covid-CT 患者数据并寻求进一步的改进。