A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans
Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and cla...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9173767/ |
| _version_ | 1818920826965065728 |
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| author | Wenming Cao Rui Wu Guitao Cao Zhihai He |
| author_facet | Wenming Cao Rui Wu Guitao Cao Zhihai He |
| author_sort | Wenming Cao |
| collection | DOAJ |
| description | Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which helps to reduce mortality. Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. In this article, a comprehensive review of the application and development of CAD systems is presented. The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics and various medical competitions. We then introduce the main structure of a CAD system and present some efficient methodologies. For the extensive use of Convolutional Neural Network (CNN) based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. Besides, we mainly select the CAD systems developed by state-of-the-art CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed. |
| first_indexed | 2024-12-20T01:27:56Z |
| format | Article |
| id | doaj.art-04aad21b483043d2973d6d8d41f6ae67 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:27:56Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-04aad21b483043d2973d6d8d41f6ae672022-12-21T19:58:11ZengIEEEIEEE Access2169-35362020-01-01815400715402310.1109/ACCESS.2020.30186669173767A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography ScansWenming Cao0https://orcid.org/0000-0002-8174-6167Rui Wu1https://orcid.org/0000-0002-1678-5645Guitao Cao2https://orcid.org/0000-0002-4059-4806Zhihai He3https://orcid.org/0000-0002-2647-8286Guangdong Multimedia Information Service Engineering Technology Research Center, Shenzhen University, Shenzhen, ChinaGuangdong Multimedia Information Service Engineering Technology Research Center, Shenzhen University, Shenzhen, ChinaShanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, ChinaDepartment of Electrical and Computer Engineering, Video Processing and Communication Laboratory, University of Missouri, Columbia, MO, USALung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which helps to reduce mortality. Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. In this article, a comprehensive review of the application and development of CAD systems is presented. The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics and various medical competitions. We then introduce the main structure of a CAD system and present some efficient methodologies. For the extensive use of Convolutional Neural Network (CNN) based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. Besides, we mainly select the CAD systems developed by state-of-the-art CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed.https://ieeexplore.ieee.org/document/9173767/CADlung cancerpulmonary nodules detectionclassificationCT scansCNN |
| spellingShingle | Wenming Cao Rui Wu Guitao Cao Zhihai He A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans IEEE Access CAD lung cancer pulmonary nodules detection classification CT scans CNN |
| title | A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans |
| title_full | A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans |
| title_fullStr | A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans |
| title_full_unstemmed | A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans |
| title_short | A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans |
| title_sort | comprehensive review of computer aided diagnosis of pulmonary nodules based on computed tomography scans |
| topic | CAD lung cancer pulmonary nodules detection classification CT scans CNN |
| url | https://ieeexplore.ieee.org/document/9173767/ |
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