Automated bleeding detection in wireless capsule endoscopy images based on color feature extraction from Gaussian mixture model superpixels

Rathnamala, S.; Jenicka, S.

doi:10.1007/s11517-021-02352-8

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…( 36 ) formulated a conventional binary SVM classifier which was trained with seven features such as color and texture attributes obtained from the Gaussian model super pixels of the images belonging to WCE.…”

Section: Resultsmentioning

confidence: 99%

Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images

A¹,

Poongodi

Bourouis

et al. 2022

Front. Oncol.

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Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)

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Section: Resultsmentioning

confidence: 99%

Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images

A¹,

Poongodi

Bourouis

et al. 2022

Front. Oncol.

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“…Other models utilising multi-layer perceptrons (MLP) [8] and back-propagation neural networks [4] have also been replaced with deep learning, with this shift appearing to primarily have occurred post-2016. Only four SVM-based models [9][10][11][12] were constructed following 2016, compared with eight CNN models [13][14][15][16][17][18][19][20] and two Kernel Neural Networks [21,22] . For example, in 2021, Ghosh et al constructed a CNN-based deep learning framework via the CNN architecture AlexNet, achieving a sensitivity of 97.51% and specificity of 99.88%, significantly enhanced from the sensitivity of approximately 80% previously mentioned by Girithiran et al [3,17] .…”

Section: Active Gi Bleedingmentioning

confidence: 99%

Artificial intelligence in capsule endoscopy: development status and future expectations

George,

Tan,

Kovoor

et al. 2024

Mini-invasive Surg

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In this review, we aim to illustrate the state-of-the-art artificial intelligence (AI) applications in the field of capsule endoscopy. AI has made significant strides in gastrointestinal imaging, particularly in capsule endoscopy - a non-invasive procedure for capturing gastrointestinal tract images. However, manual analysis of capsule endoscopy videos is labour-intensive and error-prone, prompting the development of automated computational algorithms and AI models. While currently serving as a supplementary observer, AI has the capacity to evolve into an autonomous, integrated reading system, potentially significantly reducing capsule reading time while surpassing human accuracy. We searched Embase, Pubmed, Medline, and Cochrane databases from inception to 06 Jul 2023 for studies investigating the use of AI for capsule endoscopy and screened retrieved records for eligibility. Quantitative and qualitative data were extracted and synthesised to identify current themes. In the search, 824 articles were collected, and 291 duplicates and 31 abstracts were deleted. After a double-screening process and full-text review, 106 publications were included in the review. Themes pertaining to AI for capsule endoscopy included active gastrointestinal bleeding, erosions and ulcers, vascular lesions and angiodysplasias, polyps and tumours, inflammatory bowel disease, coeliac disease, hookworms, bowel prep assessment, and multiple lesion detection. This review provides current insights into the impact of AI on capsule endoscopy as of 2023. AI holds the potential for faster and precise readings and the prospect of autonomous image analysis. However, careful consideration of diagnostic requirements and potential challenges is crucial. The untapped potential within vision transformer technology hints at further evolution and even greater patient benefit.

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“…Rathnamala et al presented a model based on Gaussian mixture model superpixels and SVM for automatic bleeding detection using CE images. First, the model classified bleeding and non-bleeding images, and then it applied the post-segmentation technique to detect the bleeding region in the bleeding image [ 45 ]. Two deep learning CNN-based models, AlexNet and SegNet, were presented in [ 46 ] to classify bleeding images and zones in CE images.…”

Section: Review Findingsmentioning

confidence: 99%

“…Ten features, including Normalized Excessive Red (NER), Hue, sum RGB, chroma, etc., were used to analyze CE video frames in [ 111 ]. 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 ].…”

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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Automated bleeding detection in wireless capsule endoscopy images based on color feature extraction from Gaussian mixture model superpixels

Cited by 12 publications

References 29 publications

Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images

Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images

Artificial intelligence in capsule endoscopy: development status and future expectations

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

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