The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy
At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very cha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/11/4/694 |
| _version_ | 1797537808153313280 |
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| author | Xuejiao Pang Zijian Zhao Ying Weng |
| author_facet | Xuejiao Pang Zijian Zhao Ying Weng |
| author_sort | Xuejiao Pang |
| collection | DOAJ |
| description | At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research. |
| first_indexed | 2024-03-10T12:21:31Z |
| format | Article |
| id | doaj.art-b8fe3df294584af1b5993af9d7bb9b87 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T12:21:31Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-b8fe3df294584af1b5993af9d7bb9b872023-11-21T15:29:13ZengMDPI AGDiagnostics2075-44182021-04-0111469410.3390/diagnostics11040694The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal EndoscopyXuejiao Pang0Zijian Zhao1Ying Weng2School of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Computer Science, University of Nottingham, Nottingham NG7 2RD, UKAt present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.https://www.mdpi.com/2075-4418/11/4/694artificial intelligencecomputer-aided diagnosis systemdeep learningesophageal lesiongastric lesiongastrointestinal endoscopy |
| spellingShingle | Xuejiao Pang Zijian Zhao Ying Weng The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy Diagnostics artificial intelligence computer-aided diagnosis system deep learning esophageal lesion gastric lesion gastrointestinal endoscopy |
| title | The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy |
| title_full | The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy |
| title_fullStr | The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy |
| title_full_unstemmed | The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy |
| title_short | The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy |
| title_sort | role and impact of deep learning methods in computer aided diagnosis using gastrointestinal endoscopy |
| topic | artificial intelligence computer-aided diagnosis system deep learning esophageal lesion gastric lesion gastrointestinal endoscopy |
| url | https://www.mdpi.com/2075-4418/11/4/694 |
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