Performance assessment of a bleeding detection algorithm for endoscopic video based on classifier fusion method and exhaustive feature selection

Deeba, Farah; Islam, Monzurul; Bui, Francis M.; Wahid, Khan A.

doi:10.1016/j.bspc.2017.10.011

Cited by 34 publications

(26 citation statements)

References 17 publications

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“…In the second stage, the bleeding regions are localised using a saliency map that indicates regions of importance within the image, estimated based on colour information from various colour spaces. A classifier fusion algorithm to detect the bleeding frames and localise the bleeding area was proposed by Deeba et al [47]. It combines the results of two classifiers trained using first-order statistical features extracted from RGB and HSV colour spaces, which include mean, standard deviation, entropy, skew, and energy.…”

Section: Blood Detection Methodsmentioning

confidence: 99%

“…A classifier fusion algorithm to detect the bleeding frames and localise the bleeding area was proposed by Deeba et al . [ 47 ]. It combines the results of two classifiers trained using first-order statistical features extracted from RGB and HSV colour spaces, which include mean, standard deviation, entropy, skew, and energy.…”

Section: State-of-the-art Abnormality Detection Softwarementioning

confidence: 99%

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The future of capsule endoscopy in clinical practice: from diagnostic to therapeutic experimental prototype capsules

Vasilakakis

Koulaouzidis

Marlicz

et al. 2020

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Capsule endoscopy (CE) is indicated as a first-line clinical examination for the detection of small-bowel pathology, and there is an ever-growing drive for it to become a method for the screening of the entire gastrointestinal tract (GI). Although CE's main function is diagnosis, the research for therapeutic capabilities has intensified to make therapeutic capsule endoscopy (TCE) a target within reach. This manuscript presents the research evolution of CE and TCE through the last 5 years and describes notable problems, as well as clinical and technological challenges to overcome. This review also reports the state-of-the-art of capsule devices with a focus on CE research prototypes promising an enhanced diagnostic yield (DY) and treatment. Lastly, this article provides an overview of the research progress made in software for enhancing DY by increasing the accuracy of abnormality detection and lesion localisation. The future of capsule endoscopy-experimental devices for diagnosis and therapy Gastroenterology Review

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Section: Blood Detection Methodsmentioning

confidence: 99%

Section: State-of-the-art Abnormality Detection Softwarementioning

confidence: 99%

The future of capsule endoscopy in clinical practice: from diagnostic to therapeutic experimental prototype capsules

Vasilakakis

Koulaouzidis

Marlicz

et al. 2020

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show abstract

“…HSV color space has resistance to illumination changes (Chen & Lee, ) which helps in WCE imaging for good results. Deeba, Islam, Bui, and Wahid () introduced a technique based on color features and fusion of classifier to detect bleeding areas in WCE images. This method achieves an average accuracy and sensitivity of 95, and 94%, respectively.…”

Section: Related Workmentioning

confidence: 99%

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Majid

Khan

Yasmin

et al. 2020

Microscopy Res & Technique

126

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Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer-aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi-type features extraction, fusion, and robust features selection. Five key steps are performed-database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16-based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K-Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets-Kvasir, CVC-ClinicDB, Private, and ETIS-LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%. K E Y W O R D S

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“…Later the same authors (Yuan, Li & Meng, 2015) added the complete LBP (CLBP), LBP, uniform LBP (ULBP), and histogram of oriented gradients (HoG) features along with SIFT features to extract additional distinctive texture features. Alternatively, color-based features were extracted in (Ghosh, Fattah & Wahid, 2018;Deeba et al, 2018) for bleeding detection.…”

Section: Introductionmentioning

confidence: 99%

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

Attallah¹,

Sharkas²

2021

PeerJ Computer Science

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Gastrointestinal (GI) diseases are common illnesses that affect the GI tract. Diagnosing these GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis (CADx) system based on deep learning (DL) techniques could considerably lower the examination cost processes and increase the speed and quality of diagnosis. Therefore, this article proposes a CADx system called Gastro-CADx to classify several GI diseases using DL techniques. Gastro-CADx involves three progressive stages. Initially, four different CNNs are used as feature extractors to extract spatial features. Most of the related work based on DL approaches extracted spatial features only. However, in the following phase of Gastro-CADx, features extracted in the first stage are applied to the discrete wavelet transform (DWT) and the discrete cosine transform (DCT). DCT and DWT are used to extract temporal-frequency and spatial-frequency features. Additionally, a feature reduction procedure is performed in this stage. Finally, in the third stage of the Gastro-CADx, several combinations of features are fused in a concatenated manner to inspect the effect of feature combination on the output results of the CADx and select the best-fused feature set. Two datasets referred to as Dataset I and II are utilized to evaluate the performance of Gastro-CADx. Results indicated that Gastro-CADx has achieved an accuracy of 97.3% and 99.7% for Dataset I and II respectively. The results were compared with recent related works. The comparison showed that the proposed approach is capable of classifying GI diseases with higher accuracy compared to other work. Thus, it can be used to reduce medical complications, death-rates, in addition to the cost of treatment. It can also help gastroenterologists in producing more accurate diagnosis while lowering inspection time.

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Performance assessment of a bleeding detection algorithm for endoscopic video based on classifier fusion method and exhaustive feature selection

Cited by 34 publications

References 17 publications

The future of capsule endoscopy in clinical practice: from diagnostic to therapeutic experimental prototype capsules

The future of capsule endoscopy in clinical practice: from diagnostic to therapeutic experimental prototype capsules

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

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