An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning
Gastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/12/11/2827 |
| _version_ | 1797465556832485376 |
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| author | Hsu-Heng Yen Hui-Yu Tsai Chi-Chih Wang Ming-Chang Tsai Ming-Hseng Tseng |
| author_facet | Hsu-Heng Yen Hui-Yu Tsai Chi-Chih Wang Ming-Chang Tsai Ming-Hseng Tseng |
| author_sort | Hsu-Heng Yen |
| collection | DOAJ |
| description | Gastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfully to help physicians with clinical diagnosis. This study combines deep learning and machine learning techniques and proposes a two-stage process for endoscopic classification in GERD, including transfer learning techniques applied to the target dataset to extract more precise image features and machine learning algorithms to build the best classification model. The experimental results demonstrate that the performance of the GerdNet-RF model proposed in this work is better than that of previous studies. Test accuracy can be improved from 78.8% ± 8.5% to 92.5% ± 2.1%. By enhancing the automated diagnostic capabilities of AI models, patient health care will be more assured. |
| first_indexed | 2024-03-09T18:23:10Z |
| format | Article |
| id | doaj.art-1efea96c1f734a569a449eb58a35fff7 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T18:23:10Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-1efea96c1f734a569a449eb58a35fff72023-11-24T08:04:55ZengMDPI AGDiagnostics2075-44182022-11-011211282710.3390/diagnostics12112827An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine LearningHsu-Heng Yen0Hui-Yu Tsai1Chi-Chih Wang2Ming-Chang Tsai3Ming-Hseng Tseng4Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, TaiwanDepartment of Medical Informatics, Chung Shan Medical University, Taichung 402, TaiwanInstitute of Medicine, Chung Shan Medical University, Taichung 402, TaiwanInstitute of Medicine, Chung Shan Medical University, Taichung 402, TaiwanDepartment of Medical Informatics, Chung Shan Medical University, Taichung 402, TaiwanGastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfully to help physicians with clinical diagnosis. This study combines deep learning and machine learning techniques and proposes a two-stage process for endoscopic classification in GERD, including transfer learning techniques applied to the target dataset to extract more precise image features and machine learning algorithms to build the best classification model. The experimental results demonstrate that the performance of the GerdNet-RF model proposed in this work is better than that of previous studies. Test accuracy can be improved from 78.8% ± 8.5% to 92.5% ± 2.1%. By enhancing the automated diagnostic capabilities of AI models, patient health care will be more assured.https://www.mdpi.com/2075-4418/12/11/2827gastroesophageal reflux diseasedeep learningtransfer learningmachine learninghealthcare |
| spellingShingle | Hsu-Heng Yen Hui-Yu Tsai Chi-Chih Wang Ming-Chang Tsai Ming-Hseng Tseng An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning Diagnostics gastroesophageal reflux disease deep learning transfer learning machine learning healthcare |
| title | An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning |
| title_full | An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning |
| title_fullStr | An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning |
| title_full_unstemmed | An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning |
| title_short | An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning |
| title_sort | improved endoscopic automatic classification model for gastroesophageal reflux disease using deep learning integrated machine learning |
| topic | gastroesophageal reflux disease deep learning transfer learning machine learning healthcare |
| url | https://www.mdpi.com/2075-4418/12/11/2827 |
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