Detection of abnormalities in wireless capsule endoscopy based on extreme learning machine

Ellahyani, Ayoub; Jaafari, Ilyas El; Charfi, Said; Ansari, Mohamed El

doi:10.1007/s11760-020-01809-x

Cited by 21 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ellahyani et al [18] proposed a WCE abnormality detection system based on an extreme learning machine. In the preprocessing, they use the hue component of HSV color space to apply oriented gradients (HOG) and a modifed rotation-invariant local binary pattern for feature extraction and then combined the features as a vector and feed them to the Kernel ELM classifer.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction for Polyp Detection in Wireless Capsule Endoscopy Video Frames

Delagah,

Hassanpour

2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Wireless capsule endoscopy (WCE) is a technology for filming the gastrointestinal (GI) tract to find abnormalities such as tumors, polyps, and bleeding. This paper proposes a new method based on hand-crafted features to detect polyps in WCE frames. A polyp has a convex surface containing pixel values with a specified Gaussian distribution. If a polyp exists in the WCE image, edges will be seen at the border of the occupied area. Since WCE images often suffer from low illumination, a histogram equalization (HE) technique can be used to enhance the image. In this paper, we initially find probable polyp edges via thresholding. Then, we use the edges to find the region of interest (ROI). Then, the mean, standard deviation (STD), and division of mean by STD from the ROI are computed as features to discriminate between polyp and nonpolyp using a support vector machine (SVM). The evaluation results on the Kvasir-Capsule dataset show 99% accuracy for the proposed method in polyp detection. Furthermore, the proposed method runs at a real-time speed of ∼0.031 seconds detection for each image.

show abstract

Section: Related Workmentioning

confidence: 99%

Feature Extraction for Polyp Detection in Wireless Capsule Endoscopy Video Frames

Delagah,

Hassanpour

2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

Feature Pyramid Network Based Spatial Attention and Cross‐Level Semantic Similarity for Diseases Segmentation From Capsule Endoscopy Images

Charfi,

EL Ansari,

Koutti

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

As an emerging technology that uses a pill‐sized camera to visualize images of the digestive tract. Wireless capsule endoscopy (WCE) presents several advantages, since it is far less invasive, does not need sedation and has less possible complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro‐intestinal diseases such as polyps, ulcers, crohns disease, and hemorrhages. Nevertheless, WCE videos produced after a test may consist of thousands of frames per patient that must be viewed by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. Hence, development of an automatic tool based on artificial intelligence might be very helpful. Moreover, most state‐of‐the‐art works aim at images classification (normal/abnormal) while ignoring diseases segmentation. Therefore, in this work a novel method based on Feature Pyramid Network model is presented. This approach aims at diseases segmentation from WCE images. In this model, modules to optimize and combine features were employed. Specifically, semantic and spatial features were mutually compensated by spatial attention and cross‐level global feature fusion modules. The proposed method testing F1‐score and mean intersection over union are 94.149% and 89.414%, respectively, in the MICCAI 2017 dataset. In the KID Atlas dataset, the method achieved a testing F1‐score and mean intersection over union of 94.557% and 90.416%, respectively. Through the performance analysis, the mean intersection over union in the MICCAI 2017 dataset is 20.414%, 18.484%, 11.444%, 8.794% more than existing approaches. Moreover, the proposed scheme surpassed the methods used for comparison by 29.986% and 9.416% in terms of mean intersection over union in KID Atlas dataset. These results indicate that the proposed approach is promising in diseases segmentation from WCE images.

show abstract

Optimal feature extraction and ulcer classification from WCE image data using deep learning

et al. 2022

View full text Add to dashboard Cite

Detection of abnormalities in wireless capsule endoscopy based on extreme learning machine

Cited by 21 publications

References 20 publications

Feature Extraction for Polyp Detection in Wireless Capsule Endoscopy Video Frames

Feature Extraction for Polyp Detection in Wireless Capsule Endoscopy Video Frames

Feature Pyramid Network Based Spatial Attention and Cross‐Level Semantic Similarity for Diseases Segmentation From Capsule Endoscopy Images

Optimal feature extraction and ulcer classification from WCE image data using deep learning

Contact Info

Product

Resources

About