Biomedical Engineering 2017
DOI: 10.2316/p.2017.852-031
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Automatic Polyp Detection in Endoscopy Videos: A Survey

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Cited by 30 publications
(10 citation statements)
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“…Through a human-machine competition, the system was found to be more reliable than endoscopic physicians in diagnosis of CRC. Taha et al [ 56 ] introduced a DL solution for polyps from colonoscopy, a pre-training architecture for feature extraction, used together with the classical SVM classifier. As the solution can avoid the high computational complexity and high resource requirements of CNN, it outperforms other models in the early screening of CRC[ 56 ].…”
Section: Use Of Ai In Diagnosis Of Crcmentioning
confidence: 99%
See 1 more Smart Citation
“…Through a human-machine competition, the system was found to be more reliable than endoscopic physicians in diagnosis of CRC. Taha et al [ 56 ] introduced a DL solution for polyps from colonoscopy, a pre-training architecture for feature extraction, used together with the classical SVM classifier. As the solution can avoid the high computational complexity and high resource requirements of CNN, it outperforms other models in the early screening of CRC[ 56 ].…”
Section: Use Of Ai In Diagnosis Of Crcmentioning
confidence: 99%
“…Taha et al [ 56 ] introduced a DL solution for polyps from colonoscopy, a pre-training architecture for feature extraction, used together with the classical SVM classifier. As the solution can avoid the high computational complexity and high resource requirements of CNN, it outperforms other models in the early screening of CRC[ 56 ]. Yao et al [ 57 ] proved that the features in red, green, blue (RGB) and HSV color space could well describe the frames in colonoscopy videos.…”
Section: Use Of Ai In Diagnosis Of Crcmentioning
confidence: 99%
“…Colonoscopy is the screening method used to detect and identify tumors throughout the colon [10]. During this colonoscopy procedure, the doctor checks the abnormality for tumors.…”
Section: Colonoscopymentioning
confidence: 99%
“…Deep learning-based methods are the current state-of-the-art methodology for almost all image understanding and analysis problems like semantic segmentation, image recognition and classification [6,11]. In gastroenterology, AI methods have been explored from both the classical model-driven and deep learning paradigms [20]. While the majority of work has focused on the detection or delineation of diseased regions [5,14,27], on the measurement of structural size [10] or the 3D navigation within the endoluminal organs [12,15,25], relatively little research effort has been invested into the classification of different endoscopic viewpoints that need to be viewed to complete an examination.…”
Section: Introductionmentioning
confidence: 99%