Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review
Objective: For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the pictu...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/13/9/1911 |
| _version_ | 1797579202575204352 |
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| author | Hailun Liang Meili Hu Yuxin Ma Lei Yang Jie Chen Liwei Lou Chen Chen Yuan Xiao |
| author_facet | Hailun Liang Meili Hu Yuxin Ma Lei Yang Jie Chen Liwei Lou Chen Chen Yuan Xiao |
| author_sort | Hailun Liang |
| collection | DOAJ |
| description | Objective: For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the picture (extraction of fake features, etc.). Our goal is to investigate how well deep-learning approaches classify lung nodule malignancy. Method: We evaluated the performance of deep-learning methods on lung nodule malignancy classification via a systematic literature search. We conducted searches for appropriate articles in the PubMed and ISI Web of Science databases and chose those that employed deep learning to classify or predict lung nodule malignancy for our investigation. The figures were plotted, and the data were extracted using SAS version 9.4 and Microsoft Excel 2010, respectively. Results: Sixteen studies that met the criteria were included in this study. The articles classified or predicted pulmonary nodule malignancy using classification and summarization, using convolutional neural network (CNN), autoencoder (AE), and deep belief network (DBN). The AUC of deep-learning models is typically greater than 90% in articles. It demonstrated that deep learning performed well in the diagnosis and forecasting of lung nodules. Conclusion: It is a thorough analysis of the most recent advancements in lung nodule deep-learning technologies. The advancement of image processing techniques, traditional machine learning techniques, deep-learning techniques, and other techniques have all been applied to the technology for pulmonary nodule diagnosis. Although the deep-learning model has demonstrated distinct advantages in the detection of pulmonary nodules, it also carries significant drawbacks that warrant additional research. |
| first_indexed | 2024-03-10T22:32:34Z |
| format | Article |
| id | doaj.art-fcfa162f512a4a91a9e8f0318d39b54d |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-10T22:32:34Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-fcfa162f512a4a91a9e8f0318d39b54d2023-11-19T11:38:00ZengMDPI AGLife2075-17292023-09-01139191110.3390/life13091911Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic ReviewHailun Liang0Meili Hu1Yuxin Ma2Lei Yang3Jie Chen4Liwei Lou5Chen Chen6Yuan Xiao7School of Public Administration and Policy, Renmin University of China, Beijing 100872, ChinaDepartment of Gynecology, Baoding Maternal and Child Health Care Hospital, Baoding 071000, ChinaSchool of Public Administration and Policy, Renmin University of China, Beijing 100872, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, ChinaSchool of Public Administration and Policy, Renmin University of China, Beijing 100872, ChinaSchool of Statistics, Renmin University of China, Beijing 100872, ChinaSchool of Public Administration and Policy, Renmin University of China, Beijing 100872, ChinaBlockchain Research Institute, Renmin University of China, Beijing 100872, ChinaObjective: For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the picture (extraction of fake features, etc.). Our goal is to investigate how well deep-learning approaches classify lung nodule malignancy. Method: We evaluated the performance of deep-learning methods on lung nodule malignancy classification via a systematic literature search. We conducted searches for appropriate articles in the PubMed and ISI Web of Science databases and chose those that employed deep learning to classify or predict lung nodule malignancy for our investigation. The figures were plotted, and the data were extracted using SAS version 9.4 and Microsoft Excel 2010, respectively. Results: Sixteen studies that met the criteria were included in this study. The articles classified or predicted pulmonary nodule malignancy using classification and summarization, using convolutional neural network (CNN), autoencoder (AE), and deep belief network (DBN). The AUC of deep-learning models is typically greater than 90% in articles. It demonstrated that deep learning performed well in the diagnosis and forecasting of lung nodules. Conclusion: It is a thorough analysis of the most recent advancements in lung nodule deep-learning technologies. The advancement of image processing techniques, traditional machine learning techniques, deep-learning techniques, and other techniques have all been applied to the technology for pulmonary nodule diagnosis. Although the deep-learning model has demonstrated distinct advantages in the detection of pulmonary nodules, it also carries significant drawbacks that warrant additional research.https://www.mdpi.com/2075-1729/13/9/1911lung nodulesdeep learningconvolutional neural networklow-dose CT |
| spellingShingle | Hailun Liang Meili Hu Yuxin Ma Lei Yang Jie Chen Liwei Lou Chen Chen Yuan Xiao Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review Life lung nodules deep learning convolutional neural network low-dose CT |
| title | Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review |
| title_full | Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review |
| title_fullStr | Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review |
| title_full_unstemmed | Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review |
| title_short | Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review |
| title_sort | performance of deep learning solutions on lung nodule malignancy classification a systematic review |
| topic | lung nodules deep learning convolutional neural network low-dose CT |
| url | https://www.mdpi.com/2075-1729/13/9/1911 |
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