Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video
| Autor: | Ashok Vajravelu, K.S. Tamil Selvan, Muhammad Mahadi Bin Abdul Jamil, Anitha Jude, Isabel de la Torre Diez |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 44:353-364 |
| ISSN: | 1875-8967 1064-1246 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|