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Journal articles on the topic 'WCE images'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Faghih Dinevari, Vahid, Ghader Karimian Khosroshahi, and Mina Zolfy Lighvan. "Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images." Applied Bionics and Biomechanics 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3678913.

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Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD)

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Ahmad, Bilal, Pål Anders Floor, Ivar Farup, and Casper Find Andersen. "Single-Image-Based 3D Reconstruction of Endoscopic Images." Journal of Imaging 10, no. 4 (2024): 82. http://dx.doi.org/10.3390/jimaging10040082.

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A wireless capsule endoscope (WCE) is a medical device designed for the examination of the human gastrointestinal (GI) tract. Three-dimensional models based on WCE images can assist in diagnostics by effectively detecting pathology. These 3D models provide gastroenterologists with improved visualization, particularly in areas of specific interest. However, the constraints of WCE, such as lack of controllability, and requiring expensive equipment for operation, which is often unavailable, pose significant challenges when it comes to conducting comprehensive experiments aimed at evaluating the q

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Nouman Noor, Muhammad, Muhammad Nazir, Sajid Ali Khan, Oh-Young Song, and Imran Ashraf. "Efficient Gastrointestinal Disease Classification Using Pretrained Deep Convolutional Neural Network." Electronics 12, no. 7 (2023): 1557. http://dx.doi.org/10.3390/electronics12071557.

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Gastrointestinal (GI) tract diseases are on the rise in the world. These diseases can have fatal consequences if not diagnosed in the initial stages. WCE (wireless capsule endoscopy) is the advanced technology used to inspect gastrointestinal diseases such as ulcerative-colitis, polyps, esophagitis, and ulcers. WCE produces thousands of frames for a single patient’s procedure for which manual examination is tiresome, time-consuming, and prone to error; therefore, an automated procedure is needed. WCE images suffer from low contrast which increases inter-class and intra-class similarity and red

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Pan, Guo Bing, Fang Xu, and Jiao Liao Chen. "Bleeding Detection in Wireless Capsule Endoscopy Using Color Similarity Coefficient." Applied Mechanics and Materials 195-196 (August 2012): 307–12. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.307.

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Wireless Capsule Endoscopy (WCE) generates a large number of images in one examination of a patient. It is very laborious and time-consuming to detect the WCE video, and limits the wider application of WCE. It is urgent and necessary to develop an automatic and intelligent computer aided bleeding detection technique. This paper proposes the color vector similarity coefficients to measure the color similarity, and based on which, a novel algorithm is implemented to recognize the bleeding in WCE images. The novel algorithm is implemented in RGB color space, and is featured with simple computatio

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Zhang, Shaojie, Yinghui Wang, Peixuan Liu, et al. "Capsule Endoscopy Image Enhancement for Small Intestinal Villi Clarity." Mathematics 12, no. 21 (2024): 3317. http://dx.doi.org/10.3390/math12213317.

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Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the l

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Vasilakakis, Michael D., Dimitris K. Iakovidis, Evaggelos Spyrou, and Anastasios Koulaouzidis. "DINOSARC: Color Features Based on Selective Aggregation of Chromatic Image Components for Wireless Capsule Endoscopy." Computational and Mathematical Methods in Medicine 2018 (September 3, 2018): 1–11. http://dx.doi.org/10.1155/2018/2026962.

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Wireless Capsule Endoscopy (WCE) is a noninvasive diagnostic technique enabling the inspection of the whole gastrointestinal (GI) tract by capturing and wirelessly transmitting thousands of color images. Proprietary software “stitches” the images into videos for examination by accredited readers. However, the videos produced are of large length and consequently the reading task becomes harder and more prone to human errors. Automating the WCE reading process could contribute in both the reduction of the examination time and the improvement of its diagnostic accuracy. In this paper, we present

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Meziou, L., A. Histace, F. Precioso, et al. "Computer-Assisted Segmentation of Videocapsule Images Using Alpha-Divergence-Based Active Contour in the Framework of Intestinal Pathologies Detection." International Journal of Biomedical Imaging 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/428583.

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Visualization of the entire length of the gastrointestinal tract through natural orifices is a challenge for endoscopists. Videoendoscopy is currently the “gold standard” technique for diagnosis of different pathologies of the intestinal tract. Wireless capsule endoscopy (WCE) has been developed in the 1990s as an alternative to videoendoscopy to allow direct examination of the gastrointestinal tract without any need for sedation. Nevertheless, the systematic postexamination by the specialist of the 50,000 (for the small bowel) to 150,000 images (for the colon) of a complete acquisition using

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Kundu, Amit Kumar, Shaikh Anowarul Fattah, and Mamshad Nayeem Rizve. "An Automatic Bleeding Frame and Region Detection Scheme for Wireless Capsule Endoscopy Videos Based on Interplane Intensity Variation Profile in Normalized RGB Color Space." Journal of Healthcare Engineering 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/9423062.

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Wireless capsule endoscopy (WCE) is an effective video technology to diagnose gastrointestinal (GI) disease, such as bleeding. In order to avoid conventional tedious and risky manual review process of long duration WCE videos, automatic bleeding detection schemes are getting importance. In this paper, to investigate bleeding, the analysis of WCE images is carried out in normalized RGB color space as human perception of bleeding is associated with different shades of red. In the proposed method, at first, from the WCE image frame, an efficient region of interest (ROI) is extracted based on inte

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Alaskar, Haya, Abir Hussain, Nourah Al-Aseem, Panos Liatsis, and Dhiya Al-Jumeily. "Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images." Sensors 19, no. 6 (2019): 1265. http://dx.doi.org/10.3390/s19061265.

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Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The me

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Rathnamala, Dr S., Aghila Rajagopal, M. Arunachalam, and V. Dhanasekaran. "GI Bleeding Detection in WCE Images Using E-ORB and ME-DEEP CAPSNET." International Journal of Environmental Sciences 11, no. 7s (2025): 147–60. https://doi.org/10.64252/358k2389.

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Wireless Capsule Endoscopy (WCE) is utilized in the detection of several anomalies like bleeding, ulcers, polyps, and tumors in the gastrointestinal (GI) tract. As a huge number of images are produced by WCE, the manual examination becomes much more tedious, time-consuming, and furthermore increasing the possibility of human errors. Therefore, a new automated scheme to detect bleeding region in WCE images by means of deep learning technique is proposed in this approach. Initially, the WCE input images are pre-processed by means of distribution linearization and linear filtering. An Adaptive de

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Charfi, Said, Mohamed El Ansari, Ayoub Ellahyani, and Ilyas El Jaafari. "Prelimenary results of red lesion segmentation in WCE images." E3S Web of Conferences 297 (2021): 01060. http://dx.doi.org/10.1051/e3sconf/202129701060.

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Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary

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Shahril, Rosdiana, Sabariah Baharun, and AKM Muzahidul Islam. "Pre-processing Technique for Wireless Capsule Endoscopy Image Enhancement." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1617. http://dx.doi.org/10.11591/ijece.v6i4.9688.

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<span>Wireless capsule endoscopy (WCE) is used to examine human digestive tract in order to detect abnormal area. However, it has been a challenging task to detect abnormal area such as bleeding due to poor quality and dark images of WCE. In this paper, pre-processing technique is introduced to ease classification of the bleeding area. Anisotropic contrast diffusion method is employed in our pre-processing technique as a contrast enhancement of the images. There is a drawback to the method proposed B. Li in which the quality of WCE image is degraded when the number of iteration increases

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Shahril, Rosdiana, Sabariah Baharun, and AKM Muzahidul Islam. "Pre-processing Technique for Wireless Capsule Endoscopy Image Enhancement." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1617. http://dx.doi.org/10.11591/ijece.v6i4.pp1617-1626.

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<span>Wireless capsule endoscopy (WCE) is used to examine human digestive tract in order to detect abnormal area. However, it has been a challenging task to detect abnormal area such as bleeding due to poor quality and dark images of WCE. In this paper, pre-processing technique is introduced to ease classification of the bleeding area. Anisotropic contrast diffusion method is employed in our pre-processing technique as a contrast enhancement of the images. There is a drawback to the method proposed B. Li in which the quality of WCE image is degraded when the number of iteration increases

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Amiri, Zahra, Hamid Hassanpour, and Azeddine Beghdadi. "A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images." Journal of Healthcare Engineering 2021 (October 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/7863113.

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Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by us

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Wang, Sen, Yuxiang Xing, Li Zhang, Hewei Gao, and Hao Zhang. "Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization." Computational and Mathematical Methods in Medicine 2019 (September 18, 2019): 1–14. http://dx.doi.org/10.1155/2019/7546215.

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Wireless capsule endoscopy (WCE) has developed rapidly over the last several years and now enables physicians to examine the gastrointestinal tract without surgical operation. However, a large number of images must be analyzed to obtain a diagnosis. Deep convolutional neural networks (CNNs) have demonstrated impressive performance in different computer vision tasks. Thus, in this work, we aim to explore the feasibility of deep learning for ulcer recognition and optimize a CNN-based ulcer recognition architecture for WCE images. By analyzing the ulcer recognition task and characteristics of cla

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K., Vijila Rani, and Nisha M. "Hookworm and Bleeding Detection in WCE Images using Rusboost Classifier." Journal of Image Processing and Artificial Intelligence 4, no. 3 (2018): 13–19. https://doi.org/10.5281/zenodo.1744883.

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Now-a-days, million ranges of individuals are having helminthiasis and this number has been increasing day by day. Automatic hookworm recognition could be a difficult task in medical field. Here projected a completely unique technique for detective work the helminthiasis from wireless capsule examination (WCE) pictures. During this paper initial adopted for WCE image with sweetening method by mistreatment Multi-scale twin Matched Filter (MDMF). Then, Piecewise Parallel Region Detection (PPRD) is employed to discover the parallel edges. This technique is extremely appropriate for detective work

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Oh, SangYup, DongJun Oh, Dongmin Kim, et al. "Video Analysis of Small Bowel Capsule Endoscopy Using a Transformer Network." Diagnostics 13, no. 19 (2023): 3133. http://dx.doi.org/10.3390/diagnostics13193133.

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Although wireless capsule endoscopy (WCE) detects small bowel diseases effectively, it has some limitations. For example, the reading process can be time consuming due to the numerous images generated per case and the lesion detection accuracy may rely on the operators’ skills and experiences. Hence, many researchers have recently developed deep-learning-based methods to address these limitations. However, they tend to select only a portion of the images from a given WCE video and analyze each image individually. In this study, we note that more information can be extracted from the unused fra

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Dahmouni, Abdellatif, Abdelkaher Ait Abdelouahad, Yasser Aderghal, Ibrahim Guelzim, Insaf Bellamine, and Hassan Silkan. "A Robust Approach for Ulcer Classification/Detection in WCE Images." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 06 (2024): 86–102. http://dx.doi.org/10.3991/ijoe.v20i06.45773.

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Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically

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19

Sahafi, Ali, Anastasios Koulaouzidis, and Mehrshad Lalinia. "Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images." Diagnostics 14, no. 5 (2024): 474. http://dx.doi.org/10.3390/diagnostics14050474.

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Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming more common and can cause serious health problems and high healthcare costs. Small bowel tumours (SBTs) and colorectal cancer (CRC) are both becoming more prevalent, especially among younger adults. Early detection and removal of polyps (precursors of malignancy) is essential for prevention. Wireless Capsule Endoscopy (WCE) is a procedure that utilises swallowable camera devices that capture images of the GI tract. Because WCE generates a large number of images, automated polyp segmentation is crucia

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Fante, Kinde Anlay, Fetulhak Abdurahman, and Mulugeta Tegegn Gemeda. "An Ingenious Application-Specific Quality Assessment Methods for Compressed Wireless Capsule Endoscopy Images." Transactions on Environment and Electrical Engineering 4, no. 1 (2020): 18. http://dx.doi.org/10.22149/teee.v4i1.139.

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<p>Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved im

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Kim, Hyeon-Seo, Byungwoo Cho, Jong-Oh Park, and Byungjeon Kang. "Color-Transfer-Enhanced Data Construction and Validation for Deep Learning-Based Upper Gastrointestinal Landmark Classification in Wireless Capsule Endoscopy." Diagnostics 14, no. 6 (2024): 591. http://dx.doi.org/10.3390/diagnostics14060591.

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While the adoption of wireless capsule endoscopy (WCE) has been steadily increasing, its primary application remains limited to observing the small intestine, with relatively less application in the upper gastrointestinal tract. However, there is a growing anticipation that advancements in capsule endoscopy technology will lead to a significant increase in its application in upper gastrointestinal examinations. This study addresses the underexplored domain of landmark identification within the upper gastrointestinal tract using WCE, acknowledging the limited research and public datasets availa

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Souaidi, Meryem, and Mohamed El Ansari. "Multi-Scale Hybrid Network for Polyp Detection in Wireless Capsule Endoscopy and Colonoscopy Images." Diagnostics 12, no. 8 (2022): 2030. http://dx.doi.org/10.3390/diagnostics12082030.

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The trade-off between speed and precision is a key step in the detection of small polyps in wireless capsule endoscopy (WCE) images. In this paper, we propose a hybrid network of an inception v4 architecture-based single-shot multibox detector (Hyb-SSDNet) to detect small polyp regions in both WCE and colonoscopy frames. Medical privacy concerns are considered the main barriers to WCE image acquisition. To satisfy the object detection requirements, we enlarged the training datasets and investigated deep transfer learning techniques. The Hyb-SSDNet framework adopts inception blocks to alleviate

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Kuang, Shuai, Guozheng Yan, and Zhiwu Wang. "Optimization Design for Receiving Coil with Novel Structure Based on Mutual Coupling Model in Wireless Power Transmission for Capsule Endoscope." Energies 13, no. 23 (2020): 6460. http://dx.doi.org/10.3390/en13236460.

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Wireless capsule endoscope (WCE) is a promising technology for noninvasive and painless imaging detection on gastrointestinal (GI) diseases. On the other hand, conventional endoscopes with wires could discomfort patients and cause them to vomit and aerosolize coronavirus if the patients are infected with COVID-19. However, there stands a technical bottleneck on power supply for the WCE. With the help of wireless power transmission technology, a hollow receiving coil (RC) is proposed to supply sufficient power and also minimize the size of WCE. A model on mutual inductance between transmitting

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Al Mamun, A., M. S. Hossain, P. P. Em, A. Tahabilder, R. Sultana, and M. A. Islam. "Small intestine bleeding detection using color threshold and morphological operation in WCE images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3040. http://dx.doi.org/10.11591/ijece.v11i4.pp3040-3048.

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<span>Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, polyps etc in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold

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A., Al. Mamun, S. Hossain M., P. Em P., Tahabilder A., Sultana R., and A. Islam M. "Small intestine bleeding detection using color threshold and morphological operation in WCE images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3040–48. https://doi.org/10.11591/ijece.v11i4.pp3040-3048.

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Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold and mor

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Zhou, Shangbo, Han Yang, Muhammad Abubakar Siddique, Jie Xu, and Ping Zhou. "A novel method for automatically locating the pylorus in the wireless capsule endoscopy." Biomedical Engineering / Biomedizinische Technik 62, no. 1 (2017): 1–12. http://dx.doi.org/10.1515/bmt-2015-0080.

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AbstractWireless capsule endoscopy (WCE) is a non-invasive technique used to examine the interiors of digestive tracts. Generally, the digestive tract can be divided into four segments: the entrance; stomach; small intestine; and large intestine. The stomach and the small intestine have a higher risk of infections than the other segments. In order to locate the diseased organ, an appropriate classification of the WCE images is necessary. In this article, a novel method is proposed for automatically locating the pylorus in WCE. The location of the pylorus is determined on two levels: rough-leve

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Sheet, Noora Mazin, Ziyad K. Farej, and Amer Farhan Sheet. "A Survey on Techniques of Wireless Capsule Endoscopy for Image Enhancement and Disease Detection." European Journal of Computer Science and Information Technology 12, no. 2 (2024): 65–74. http://dx.doi.org/10.37745/ejcsit.2013/vol12n26574.

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Wireless capsule endoscopy (WCE) is the gold standard for diagnosing small bowel disorders and is considered the future of effective diagnostic gastrointestinal (GI) endoscopy. Patients find it comfortable and more likely to adopt it than traditional colonoscopy and gastroscopy, making it a viable option for detecting cancer or ulcerations. WCE can obtain images of the GI tract from the inside, but pinpointing the disease's location remains a challenge. This paper reviews studies on endoscopy capsule development and discusses techniques and solutions for higher efficiency. Research has demonst

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Mehedi, Ibrahim M., K. Prahlad Rao, Fahad Mushhabbab Alotaibi, and Hadi Mohsen Alkanfery. "Intelligent Wireless Capsule Endoscopy for the Diagnosis of Gastrointestinal Diseases." Diagnostics 13, no. 8 (2023): 1445. http://dx.doi.org/10.3390/diagnostics13081445.

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Through a wireless capsule endoscope (WCE) fitted with a miniature camera (about an inch), this study aims to examine the role of wireless capsule endoscopy (WCE) in the diagnosis, monitoring, and evaluation of GI (gastrointestinal) disorders. In a wearable belt recorder, a capsule travels through the digestive tract and takes pictures. It attempts to find tiny components that can be used to enhance the WCE. To accomplish this, we followed the steps below: Researching current capsule endoscopy through databases, designing and simulating the device using computers, implanting the system and fin

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Delagah, Bardia, and Hamid Hassanpour. "Feature Extraction for Polyp Detection in Wireless Capsule Endoscopy Video Frames." Journal of Healthcare Engineering 2023 (November 3, 2023): 1–9. http://dx.doi.org/10.1155/2023/6076514.

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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 probabl

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Souaidi, Meryem, and Mohamed El Ansari. "Multi-scale analysis of ulcer disease detection from WCE images." IET Image Processing 13, no. 12 (2019): 2233–44. http://dx.doi.org/10.1049/iet-ipr.2019.0415.

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Salehpour, Pedram, Hossein Balazadeh Bahar, Ghader Karimian, and Hossein Ebrahimnezhad. "ADAPTED BIT-PLANE PROBABILITY AND WAVELET-BASED ULCER DETECTION IN WIRELESS CAPSULE ENDOSCOPY IMAGES." Biomedical Engineering: Applications, Basis and Communications 28, no. 04 (2016): 1650029. http://dx.doi.org/10.4015/s1016237216500290.

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Wireless capsule endoscopy (WCE) has been proven to be a robust technology which is able to ease diagnosing the GI tract diseases. It can be seen that a better computational algorithm is needed to analyze WCE images. Ulcer is one of the several diseases which are diagnosed using these images. Non-uniform lighting can complicate the detection process because it can change the color of tissue and make it seem darker or lighter than usual. This change of color makes the detection harder as the main feature of detecting ulcer as the color of the tissue. In this research work, adapted bit-planes ar

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Mahmood, Saqib, Mian Muhammad Sadiq Fareed, Gulnaz Ahmed, et al. "A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images." Biomedicines 10, no. 9 (2022): 2195. http://dx.doi.org/10.3390/biomedicines10092195.

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Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models

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Chen, Yingju, and Jeongkyu Lee. "A Review of Machine-Vision-Based Analysis of Wireless Capsule Endoscopy Video." Diagnostic and Therapeutic Endoscopy 2012 (November 13, 2012): 1–9. http://dx.doi.org/10.1155/2012/418037.

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Wireless capsule endoscopy (WCE) enables a physician to diagnose a patient's digestive system without surgical procedures. However, it takes 1-2 hours for a gastroenterologist to examine the video. To speed up the review process, a number of analysis techniques based on machine vision have been proposed by computer science researchers. In order to train a machine to understand the semantics of an image, the image contents need to be translated into numerical form first. The numerical form of the image is known as image abstraction. The process of selecting relevant image features is often dete

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Nakada, Ayako, Ryota Niikura, Keita Otani, et al. "Improved Object Detection Artificial Intelligence Using the Revised RetinaNet Model for the Automatic Detection of Ulcerations, Vascular Lesions, and Tumors in Wireless Capsule Endoscopy." Biomedicines 11, no. 3 (2023): 942. http://dx.doi.org/10.3390/biomedicines11030942.

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The use of computer-aided detection models to diagnose lesions in images from wireless capsule endoscopy (WCE) is a topical endoscopic diagnostic solution. We revised our artificial intelligence (AI) model, RetinaNet, to better diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. RetinaNet was trained using the data of 1234 patients, consisting of images of 6476 erosions and ulcers, 1916 vascular lesions, 7127 tumors, and 14,014,149 normal tissues. The mean area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for

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Otani, Keita, Ayako Nakada, Yusuke Kurose, et al. "Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network." Endoscopy 52, no. 09 (2020): 786–91. http://dx.doi.org/10.1055/a-1167-8157.

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Abstract Background Previous computer-aided detection systems for diagnosing lesions in images from wireless capsule endoscopy (WCE) have been limited to a single type of small-bowel lesion. We developed a new artificial intelligence (AI) system able to diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. Methods We trained the deep neural network system RetinaNet on a data set of 167 patients, which consisted of images of 398 erosions and ulcers, 538 vascular lesions, 4590 tumors, and 34 437 normal tissues. We calculated the mean area under the rece

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El-Ghany, Sameh Abd, Mahmood A. Mahmood, and A. A. Abd El-Aziz. "An Accurate Deep Learning-Based Computer-Aided Diagnosis System for Gastrointestinal Disease Detection Using Wireless Capsule Endoscopy Image Analysis." Applied Sciences 14, no. 22 (2024): 10243. http://dx.doi.org/10.3390/app142210243.

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Peptic ulcers and stomach cancer are common conditions that impact the gastrointestinal (GI) system. Wireless capsule endoscopy (WCE) has emerged as a widely used, noninvasive technique for diagnosing these issues, providing valuable insights through the detailed imaging of the GI tract. Therefore, an early and accurate diagnosis of GI diseases is crucial for effective treatment. This paper introduces the Intelligent Learning Rate Controller (ILRC) mechanism that optimizes the training of deep learning (DL) models by adaptively adjusting the learning rate (LR) based on training progress. This

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Vieira, Pedro M., Catarina P. Silva, Dalila Costa, Ismael F. Vaz, Carla Rolanda, and Carlos S. Lima. "Automatic Segmentation and Detection of Small Bowel Angioectasias in WCE Images." Annals of Biomedical Engineering 47, no. 6 (2019): 1446–62. http://dx.doi.org/10.1007/s10439-019-02248-7.

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Meher, Dinesh, Mrinal Gogoi, Pankaj Bharali, Prajna Anirvan, and Shivaram Prasad Singh. "Artificial Intelligence in Small Bowel Endoscopy: Current Perspectives and Future Directions." Journal of Digestive Endoscopy 11, no. 04 (2020): 245–52. http://dx.doi.org/10.1055/s-0040-1717824.

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AbstractArtificial intelligence (AI) is a computer system that is able to perform tasks which normally require human intelligence. The role of AI in the field of gastroenterology has been gradually evolving since its inception in the 1950s. Discovery of wireless capsule endoscopy (WCE) and balloon enteroscopy (BE) has revolutionized small gut imaging. While WCE is a relatively patient-friendly and noninvasive mode to examine the nonobstructed small gut, it is limited by a lengthy examination time and the need for expertise in reading images acquired by the capsule. Similarly, BE, despite havin

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Huang, Chien-Wei, Chang-Chao Su, Chu-Kuang Chou, et al. "Software-Based Transformation of White Light Endoscopy Images to Hyperspectral Images for Improved Gastrointestinal Disease Detection." Diagnostics 15, no. 13 (2025): 1664. https://doi.org/10.3390/diagnostics15131664.

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Background/Objectives: Gastrointestinal diseases (GID), such as oesophagitis, polyps, and ulcerative colitis, contribute significantly to global morbidity and mortality. Conventional diagnostic methods employing white light imaging (WLI) in wireless capsule endoscopy (WCE) provide limited spectrum information, therefore influencing classification performance. Methods: A new technique called Spectrum Aided Vision Enhancer (SAVE), which converts traditional WLI images into hyperspectral imaging (HSI)-like representations, hence improving diagnostic accuracy. HSI involves the acquisition of image

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Chen, Hongda, Shaoze Wang, Yong Ding, and Dahong Qian. "Saliency-Based Bleeding Localization for Wireless Capsule Endoscopy Diagnosis." International Journal of Biomedical Imaging 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8147632.

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Stomach bleeding is a kind of gastrointestinal disease which can be diagnosed noninvasively by wireless capsule endoscopy (WCE). However, it requires much time for physicians to scan large amount of WCE images. Alternatively, computer-assisted bleeding localization systems are developed where color, edge, and intensity features are defined to distinguish lesions from normal tissues. This paper proposes a saliency-based localization system where three saliency maps are computed: phase congruency-based edge saliency map derived from Log-Gabor filter bands, intensity histogram-guided intensity sa

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Ponnusamy, R., S. Sathiamoorthy, and R. Visalakshi. "An efficient method to classify GI tract images from WCE using visual words." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5678. http://dx.doi.org/10.11591/ijece.v10i6.pp5678-5686.

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The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this

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R., Ponnusamy, Sathiamoorthy S., and Visalakshi R. "An efficient method to classify GI tract images from WCE using visual words." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5678–86. https://doi.org/10.11591/ijece.v10i6.pp5678-5686.

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The digital images made with the wireless capsule endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the center symmetric local binary pattern (CS-LBP) and the auto color correlogram (ACC) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve

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Rouveyre, R., T. Subileau-Langlois, T. Gomez, H. Mouchère, A. Bourreille, and C. Leberre. "P445 Wireless capsule endoscopy reading support for Crohn’s disease patients using deep neural networks with an active learning method." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i908. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0575.

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Abstract Background Artificial intelligence (AI) systems, in particular neural networks (NN), have been developed to detect lesions in wireless capsule endoscopy (WCE) in patients with Crohn’s disease (CD) in order to ease their reading. Despite acceptable performance on individual images, these tools do not analyze entire videos and require a large amount of labeled data. Active learning (AL) methods have already been used, proving their ability to limit labeling cost without reducing NN performance. The aim of this study was to evaluate several AL models in order to train an AI system able t

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Allmendinger, Alicia, Michael Spaeth, Marcus Saile, Gerassimos G. Peteinatos, and Roland Gerhards. "Agronomic and Technical Evaluation of Herbicide Spot Spraying in Maize Based on High-Resolution Aerial Weed Maps—An On-Farm Trial." Plants 13, no. 15 (2024): 2164. http://dx.doi.org/10.3390/plants13152164.

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Spot spraying can significantly reduce herbicide use while maintaining equal weed control efficacy as a broadcast application of herbicides. Several online spot-spraying systems have been developed, with sensors mounted on the sprayer or by recording the RTK-GNSS position of each crop seed. In this study, spot spraying was realized offline based on georeferenced unmanned aerial vehicle (UAV) images with high spatial resolution. Studies were conducted in four maize fields in Southwestern Germany in 2023. A randomized complete block design was used with seven treatments containing broadcast and

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Suman, Shipra, Fawnizu Hussin, Aamir Malik, et al. "Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images." Applied Sciences 7, no. 10 (2017): 1097. http://dx.doi.org/10.3390/app7101097.

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Lafraxo, Samira, Meryem Souaidi, Mohamed El Ansari, and Lahcen Koutti. "Semantic Segmentation of Digestive Abnormalities from WCE Images by Using AttResU-Net Architecture." Life 13, no. 3 (2023): 719. http://dx.doi.org/10.3390/life13030719.

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Colorectal cancer is one of the most common malignancies and the leading cause of cancer death worldwide. Wireless capsule endoscopy is currently the most frequent method for detecting precancerous digestive diseases. Thus, precise and early polyps segmentation has significant clinical value in reducing the probability of cancer development. However, the manual examination is a time-consuming and tedious task for doctors. Therefore, scientists have proposed many computational techniques to automatically segment the anomalies from endoscopic images. In this paper, we present an end-to-end 2D at

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Healthcare Engineering, Journal of. "Retracted: A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images." Journal of Healthcare Engineering 2023 (December 6, 2023): 1. http://dx.doi.org/10.1155/2023/9854302.

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LIAQAT, AMNA, MUHAMMAD ATTIQUE KHAN, JAMAL HUSSAIN SHAH, MUHAMMAD SHARIF, MUSSARAT YASMIN, and STEVEN LAWRENCE FERNANDES. "AUTOMATED ULCER AND BLEEDING CLASSIFICATION FROM WCE IMAGES USING MULTIPLE FEATURES FUSION AND SELECTION." Journal of Mechanics in Medicine and Biology 18, no. 04 (2018): 1850038. http://dx.doi.org/10.1142/s0219519418500380.

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In the area of medical imaging and computer vision, automatic diagnosis of ulcer and bleeding from wireless capsule endoscopy images has been an active research domain. It contains several challenges including low contrast, complex background, lesion shape and color which affect its segmentation and classification accuracy. In this article, a novel method for automated detection and classification of stomach infection is implemented. The proposed method consists of four major steps including preprocessing, lesion segmentation, image representation and classification. The lesion contrast is imp

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Sadeghi, Vahid, Alireza Mehridehnavi, Maryam Behdad, et al. "Multivariate Gaussian Bayes classifier with limited data for segmentation of clean and contaminated regions in the small bowel capsule endoscopy images." PLOS ONE 20, no. 3 (2025): e0315638. https://doi.org/10.1371/journal.pone.0315638.

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A considerable amount of undesirable factors in the wireless capsule endoscopy (WCE) procedure hinder the proper visualization of the small bowel and take gastroenterologists more time to review. Objective quantitative assessment of different bowel preparation paradigms and saving the physician reviewing time motivated us to present an automatic low-cost statistical model for automatically segmenting of clean and contaminated regions in the WCE images. In the model construction phase, only 20 manually pixel-labeled images have been used from the normal and reduced mucosal view classes of the K

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Silva, Juan, Aymeric Histace, Olivier Romain, Xavier Dray, and Bertrand Granado. "Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer." International Journal of Computer Assisted Radiology and Surgery 9, no. 2 (2013): 283–93. http://dx.doi.org/10.1007/s11548-013-0926-3.

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