Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform
Gastrointestinal (GI) diseases are a significant global health issue, causing millions of deaths annually. This study presents a novel method for classifying GI diseases using endoscopy videos. The proposed method involves three major phases: image processing, feature extraction, and classification....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2024/8334358 |
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| author | Biniyam Mulugeta Abuhayi Yohannes Agegnehu Bezabh Aleka Melese Ayalew Miraf Alemayehu Lakew |
| author_facet | Biniyam Mulugeta Abuhayi Yohannes Agegnehu Bezabh Aleka Melese Ayalew Miraf Alemayehu Lakew |
| author_sort | Biniyam Mulugeta Abuhayi |
| collection | DOAJ |
| description | Gastrointestinal (GI) diseases are a significant global health issue, causing millions of deaths annually. This study presents a novel method for classifying GI diseases using endoscopy videos. The proposed method involves three major phases: image processing, feature extraction, and classification. The image processing phase uses wavelet transform for segmentation and an adaptive median filter for denoising. Feature extraction is conducted using a concatenated recurrent vision transformer (RVT) with two inputs. The classification phase employs an ensemble of four classifiers: support vector machines, Bayesian network, random forest, and logistic regression. The system was trained and tested on the Hyper–Kvasir dataset, the largest publicly available GI tract image dataset, achieving an accuracy of 99.13% and an area under the curve of 0.9954. These results demonstrate a significant improvement in the accuracy and performance of GI disease classification compared to traditional methods. This study highlights the potential of combining RVTs with standard machine learning techniques and wavelet transform to enhance the automated diagnosis of GI diseases. Further validation on larger datasets and different medical environments is recommended to confirm these findings. |
| format | Article |
| id | doaj-art-87fd1213d3dc44b88a45f234147f5627 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-87fd1213d3dc44b88a45f234147f56272025-02-02T23:07:58ZengWileyAdvances in Multimedia1687-56992024-01-01202410.1155/2024/8334358Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet TransformBiniyam Mulugeta Abuhayi0Yohannes Agegnehu Bezabh1Aleka Melese Ayalew2Miraf Alemayehu Lakew3Department of Information TechnologyDepartment of Information TechnologyDepartment of Information TechnologyDepartment of Electrical and Computer EngineeringGastrointestinal (GI) diseases are a significant global health issue, causing millions of deaths annually. This study presents a novel method for classifying GI diseases using endoscopy videos. The proposed method involves three major phases: image processing, feature extraction, and classification. The image processing phase uses wavelet transform for segmentation and an adaptive median filter for denoising. Feature extraction is conducted using a concatenated recurrent vision transformer (RVT) with two inputs. The classification phase employs an ensemble of four classifiers: support vector machines, Bayesian network, random forest, and logistic regression. The system was trained and tested on the Hyper–Kvasir dataset, the largest publicly available GI tract image dataset, achieving an accuracy of 99.13% and an area under the curve of 0.9954. These results demonstrate a significant improvement in the accuracy and performance of GI disease classification compared to traditional methods. This study highlights the potential of combining RVTs with standard machine learning techniques and wavelet transform to enhance the automated diagnosis of GI diseases. Further validation on larger datasets and different medical environments is recommended to confirm these findings.http://dx.doi.org/10.1155/2024/8334358 |
| spellingShingle | Biniyam Mulugeta Abuhayi Yohannes Agegnehu Bezabh Aleka Melese Ayalew Miraf Alemayehu Lakew Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform Advances in Multimedia |
| title | Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform |
| title_full | Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform |
| title_fullStr | Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform |
| title_full_unstemmed | Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform |
| title_short | Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform |
| title_sort | classification of gastrointestinal diseases using hybrid recurrent vision transformers with wavelet transform |
| url | http://dx.doi.org/10.1155/2024/8334358 |
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