The Progress on Lung Computed Tomography Imaging Signs: A Review
Lung cancer is the highest-mortality cancer with the largest number of patients in the world. Early screening and diagnosis of lung cancer by CT imaging is of great significance to improve the cure rate of lung cancer. CT signs mean the information of comprehensive manifestations of diseases at diff...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/18/9367 |
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| author | Hanguang Xiao Yuewei Li Bin Jiang Qingling Xia Yujia Wei Huanqi Li |
| author_facet | Hanguang Xiao Yuewei Li Bin Jiang Qingling Xia Yujia Wei Huanqi Li |
| author_sort | Hanguang Xiao |
| collection | DOAJ |
| description | Lung cancer is the highest-mortality cancer with the largest number of patients in the world. Early screening and diagnosis of lung cancer by CT imaging is of great significance to improve the cure rate of lung cancer. CT signs mean the information of comprehensive manifestations of diseases at different pathological stages and levels. Automatic analysis of CT images outputs the locations and sizes of lesion regions which can help radiologists to make a credible diagnosis and effectively improve the speed and accuracy of clinical diagnosis. In this paper, we first review the domestic and foreign research progress of lung CT signs, summarize a generic structure for expressing the implementation process of existing methods, and systematically describe the signs research based on the traditional machine learning method and deep learning method. Furthermore, we provide a systematic summary and comparative analysis of the existing methods. Finally, we point out the challenges ahead and discuss the directions for improvement of future work, providing reference for scholars in related fields. |
| first_indexed | 2024-03-10T00:47:04Z |
| format | Article |
| id | doaj.art-71fd3f8a833a4524970fbf5a69613af1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:47:04Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-71fd3f8a833a4524970fbf5a69613af12023-11-23T14:57:28ZengMDPI AGApplied Sciences2076-34172022-09-011218936710.3390/app12189367The Progress on Lung Computed Tomography Imaging Signs: A ReviewHanguang Xiao0Yuewei Li1Bin Jiang2Qingling Xia3Yujia Wei4Huanqi Li5School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaLung cancer is the highest-mortality cancer with the largest number of patients in the world. Early screening and diagnosis of lung cancer by CT imaging is of great significance to improve the cure rate of lung cancer. CT signs mean the information of comprehensive manifestations of diseases at different pathological stages and levels. Automatic analysis of CT images outputs the locations and sizes of lesion regions which can help radiologists to make a credible diagnosis and effectively improve the speed and accuracy of clinical diagnosis. In this paper, we first review the domestic and foreign research progress of lung CT signs, summarize a generic structure for expressing the implementation process of existing methods, and systematically describe the signs research based on the traditional machine learning method and deep learning method. Furthermore, we provide a systematic summary and comparative analysis of the existing methods. Finally, we point out the challenges ahead and discuss the directions for improvement of future work, providing reference for scholars in related fields.https://www.mdpi.com/2076-3417/12/18/9367lung carcinomaCT imaging signs detectioncomputer-aided diagnosis (CAD)deep learningmachine learning |
| spellingShingle | Hanguang Xiao Yuewei Li Bin Jiang Qingling Xia Yujia Wei Huanqi Li The Progress on Lung Computed Tomography Imaging Signs: A Review Applied Sciences lung carcinoma CT imaging signs detection computer-aided diagnosis (CAD) deep learning machine learning |
| title | The Progress on Lung Computed Tomography Imaging Signs: A Review |
| title_full | The Progress on Lung Computed Tomography Imaging Signs: A Review |
| title_fullStr | The Progress on Lung Computed Tomography Imaging Signs: A Review |
| title_full_unstemmed | The Progress on Lung Computed Tomography Imaging Signs: A Review |
| title_short | The Progress on Lung Computed Tomography Imaging Signs: A Review |
| title_sort | progress on lung computed tomography imaging signs a review |
| topic | lung carcinoma CT imaging signs detection computer-aided diagnosis (CAD) deep learning machine learning |
| url | https://www.mdpi.com/2076-3417/12/18/9367 |
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