# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 6 Conclusions  
This paper provides a detailed review of the current research development in lung CT signs detection, with a focus on the traditional machine learning and deep learning sign research approaches. The sign research method based on traditional machine learning methods is divided into three parts: candidate region segmentation, feature extraction, and feature classification, while the deep learning methods are divided into three parts: classification and image retrieval.  
Through comparative examination of existing methodologies, current research on lung CT signatures displays the following characteristics: (1) Compared with earlier research, both machine learning and deep learning approaches have greatly improved the model performance. (2) The deep learning methods can automatically extract deeper features of the image and perform end-to-end supervised training, making it easier to achieve full automation compared to traditional machine learning methods, and the overall detection performance of the model is better than traditional methods, with more classification and image retrieval tasks. (3) The research on sign classification, segmentation, and object detection tasks is unbalanced, with substantial advances in classification tasks, while there is a scarcity of research concentrating on segmentation and object detection tasks. The fundamental explanation for this is presumably the absence of well-annotated sign-oriented datasets.  
Future research on lung signs may be conducted from the following viewpoints, based on the characteristics and development trend of present methods:  
1. Create sign-oriented datasets. The publicly accessible database LISS, in terms of signs, only comprises 271 case data, which is far from the quantity of data necessary to train deep learning networks. Therefore, building a larger-scale sign-oriented lung CT annotated dataset is vital for the development of lung CT sign recognition, especially for carrying out research on sign identification based on deep learning methods.
2. Relieve the dependency on manually labeled data, a potential strategy has to be utilized: semi-supervised learning. This technique needs to receive more attention. Since enormous success of SSL has been accomplished in the field of natural images, applying current improvements of SSL to medical imaging difficulties looks to be a viable strategy. There is a lack of material focused on semi-supervised CISs detection. It would be a good concept to transfer the semi-supervised approaches in the field of natural images to the field of medical images and to innovate on this basis.
3. Design a sign-based assisted diagnosis system for lung diseases that physicians may utilize to assess symptoms and diagnose lung abnormalities, to encourage in-depth research and development of lung sign detecting tasks using real clinical demands as a reference.  
Conceptualization, H.X. and Y.L.; investigation, Y.L., Y.W. and H.L.; writing--original draft preparation, Y.W.; writing--review and editing, Q.X. and B.J.; funding acquisition, H.X. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61971078, 51875056), Science and Technology Foundation of Chongqing Education Commission (Grant No. KJQN201901142), Chongqing Natural Science Foundation (Grant No. 2022 NSCQ-MSX1902, cstc2020 jcyj-msxm2928, 2022 NSCQ-MSX1393, 2022 NNSCQ-BHX2296), General Project (No. E010221003) of the National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College.  
Not applicable  
The authors declare no conflicts of interest.