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

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.

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

“…Using statistical features (such as mean, mode, variance, moment, entropy, energy, skewness, kurtosis, etc. ), several articles [ 1 , 30 , 61 , 71 , 78 , 79 , 94 , 96 , 103 , 109 , 115 , 116 , 122 ] extracted bleeding features from whole CE images. In [ 50 ], statistical color features of bleeding images were extracted from the RGB plane’s first-order histogram.…”

“…SVM is one of the most popular supervised ML algorithms that is used to detect bleeding and non-bleeding images or zones from CE images or videos. The majority of studies used SVM based on the extracted features of input images including color space and texture [ 119 , 122 ]. In 2008, Liu et al [ 48 ] developed an automated obscure bleeding detection technique for the GI tract that could classify bleeding and non-bleeding CE images using the SVM algorithm.…”