Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic bleeding detection

Kundu, Amit Kumar; Fattah, Shaikh Anowarul

doi:10.1016/j.compbiomed.2019.103478

Cited by 14 publications

(3 citation statements)

References 22 publications

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“…For the segmentation of bleeding regions from bleeding CE images, delta E color differences were used to extract features by applying nine color shades (red, orange, brown, maroon, purple, pink, mahogany, brown, and bittersweet) for characterizing different types of bleeding [ 45 ]. The recommended Probability Density Function (PDF) fitting-based feature extraction technique was used in the YIQ, HSV, and CIE L*a*b* color spaces [ 112 ]. In [ 113 ], 40 features were extracted from five different channels, including R in the RGB color space, V in the HSV color space, Cr in the YCbCr color space, and a and L in the CIE L*a*b* color space.…”

Section: Feature Extractionmentioning

confidence: 99%

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

Musha

Hasnat

Mamun

et al. 2023

Sensors

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Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.

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Section: Feature Extractionmentioning

confidence: 99%

“…The Pixel Of Interest (POI) technique can also extract local features that depend on the intensity values of pixels. In [ 112 , 115 ], the authors utilized POI instead of whole CE images to extract features for the classification of bleeding images.…”

Section: Feature Extractionmentioning

confidence: 99%

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

Musha

Hasnat

Mamun

et al. 2023

Sensors

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“…The proposed probability density function model fitting-based approach not only reduces computing complexity, but it also results in a more consistent representation of a class. The proposed scheme performs admirably in terms of precision, with a score of 96.77% [ 25 ]. In [ 26 ], the author proposed a Gabor capsule network for classifying complex images like the Kvasir dataset.…”

Section: Related Workmentioning

confidence: 99%

Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model

Yogapriya¹,

Sumithra

Anitha³

et al. 2021

Computational and Mathematical Methods in Medicine

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Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify diseases in the gastrointestinal tract from wireless endoscopy images in this research. We take advantage of pretrained models VGG16, ResNet-18, and GoogLeNet, a convolutional neural network (CNN) model with adjusted fully connected and output layers. The proposed models are validated with a dataset consisting of 6702 images of 8 classes. The VGG16 model achieved the highest results with 96.33% accuracy, 96.37% recall, 96.5% precision, and 96.5% F1-measure. Compared to other state-of-the-art models, the VGG16 model has the highest Matthews Correlation Coefficient value of 0.95 and Cohen’s kappa score of 0.96.

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Computer-aided system for bleeding detection in WCE images based on CNN-GRU network

Lafraxo

Ansari

Koutti

2023

Multimed Tools Appl

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Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic bleeding detection

Cited by 14 publications

References 22 publications

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

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

Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model

Computer-aided system for bleeding detection in WCE images based on CNN-GRU network

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