Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video

Vajravelu, Ashok and Tamil Selvan, K.S. and Abdul Jamil, Muhammad Mahadi and Jude, Anitha and Torre Diez, Isabel de la (2023) Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video. Journal of Intelligent & Fuzzy Systems, 44. pp. 353-364.

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Abstract

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

Item Type:	Article
Uncontrolled Keywords:	Bleeding classification and region detection, words-based color histograms, wireless capsule endoscopy estimated 35,000 per patient, the health budget has a significant economic impact, accounting for around 5 percent of all GI hemorrhages [1, 2]. The ability to picture the whole small bowel directly is an advance in diagnosing OGB. :The time taken by image screening requires clini-cians to look at 14,400–72,000 collected frames, only 1% of which may have clinical interest, which is a major drawback to the CE technique, which influ¬ences a right diagnosed approach [3]. The reporting
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	14 Feb 2023 08:28
Last Modified:	14 Feb 2023 08:28
URI:	http://eprints.uthm.edu.my/id/eprint/8309

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