Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video
Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleed...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/9019/1/J15680_b5b5a9ffedc5b53bec21607394f04dc4.pdf |
| _version_ | 1796869882154844160 |
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| author | Ashok Vajravelu, Ashok Vajravelu K.S. Tamil Selvan, K.S. Tamil Selvan Abdul Jamil, Muhammad Mahadi Anitha Judec, Anitha Judec Isabel de la Torre Diezd, Isabel de la Torre Diezd |
| author_facet | Ashok Vajravelu, Ashok Vajravelu K.S. Tamil Selvan, K.S. Tamil Selvan Abdul Jamil, Muhammad Mahadi Anitha Judec, Anitha Judec Isabel de la Torre Diezd, Isabel de la Torre Diezd |
| author_sort | Ashok Vajravelu, Ashok Vajravelu |
| collection | UTHM |
| description | Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient
without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our
approaches can correctly distinguish bleeding sites from neighborhoods. |
| first_indexed | 2024-03-05T22:01:21Z |
| format | Article |
| id | uthm.eprints-9019 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:01:21Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | uthm.eprints-90192023-06-20T03:26:27Z http://eprints.uthm.edu.my/9019/ Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video Ashok Vajravelu, Ashok Vajravelu K.S. Tamil Selvan, K.S. Tamil Selvan Abdul Jamil, Muhammad Mahadi Anitha Judec, Anitha Judec Isabel de la Torre Diezd, Isabel de la Torre Diezd T Technology (General) Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our approaches can correctly distinguish bleeding sites from neighborhoods. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9019/1/J15680_b5b5a9ffedc5b53bec21607394f04dc4.pdf Ashok Vajravelu, Ashok Vajravelu and K.S. Tamil Selvan, K.S. Tamil Selvan and Abdul Jamil, Muhammad Mahadi and Anitha Judec, Anitha Judec and Isabel de la Torre Diezd, Isabel de la Torre Diezd (2023) Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video. Journal of Intelligent & Fuzzy Systems. pp. 353-363. https://doi.org/10.3233/JIFS-213099 |
| spellingShingle | T Technology (General) Ashok Vajravelu, Ashok Vajravelu K.S. Tamil Selvan, K.S. Tamil Selvan Abdul Jamil, Muhammad Mahadi Anitha Judec, Anitha Judec Isabel de la Torre Diezd, Isabel de la Torre Diezd Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video |
| title | Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video |
| title_full | Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video |
| title_fullStr | Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video |
| title_full_unstemmed | Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video |
| title_short | Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video |
| title_sort | machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9019/1/J15680_b5b5a9ffedc5b53bec21607394f04dc4.pdf |
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