scholarly journals Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Min Min ◽  
Song Su ◽  
Wenrui He ◽  
Yiliang Bi ◽  
Zhanyu Ma ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Color Imaging ◽  
Computer Aided ◽  
Linked Color Imaging ◽  
Aided Diagnosis

Fr612 INFLUENCE OF COMPUTER AIDED DIAGNOSIS WITH AUGMENTED VISUALIZATION ON ENDOSCOPIC CLASSIFICATION OF COLORECTAL POLYPS

2021 ◽  
Vol 160 (6) ◽  
pp. S-376
Author(s):  
Eladio Rodriguez-Diaz ◽  
Gyorgy Baffy Wai-Kit Lo ◽  
Hiroshi Mashimo ◽  
Aparna Repaka ◽  
Alexander Goldowsky ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Computer Aided ◽  
Endoscopic Classification ◽  
Aided Diagnosis

Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

2021 ◽  
Author(s):  
Kamyab Keshtkar
Keyword(s):  
Colorectal Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Support Vector ◽  
Training Process ◽  
Computer Aided ◽  
Conventional Models ◽  
Aided Diagnosis

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

Su1705 REAL-TIME COMPUTER-AIDED DIAGNOSIS OF DIMINUTIVE COLORECTAL POLYPS USING AN AUTOFLUORESCENCE IMAGING SYSTEM

2019 ◽  
Vol 89 (6) ◽  
pp. AB387
Author(s):  
Hideka Horiuchi ◽  
Naoto Tamai ◽  
Shunsuke Kamba ◽  
Hiroko Inomata ◽  
Tomohiko R. Ohya ◽  
...  
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Autofluorescence Imaging ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided classification of colorectal polyps using blue-light and linked-color imaging

2019 ◽  
Author(s):  
Thom Scheeve ◽  
Ramon-Michel Schreuder ◽  
Fons van der Sommen ◽  
Joep E. G. IJspeert ◽  
Evelien Dekker ◽  
...  
Keyword(s):  
Blue Light ◽  
Colorectal Polyps ◽  
Color Imaging ◽  
Computer Aided ◽  
Linked Color Imaging

IMPROVING OPTICAL DIAGNOSIS OF COLORECTAL POLYPS USING COMPUTER-AIDED DIAGNOSIS (CADX)

2020 ◽  
Author(s):  
QEW van der Zander ◽  
RM Schreuder ◽  
R Fonolla ◽  
T Scheeve ◽  
F van der Sommen ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Optical Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared to intuitive optical diagnosis

Endoscopy ◽  
2020 ◽  
Author(s):  
Quirine E.W. van der Zander ◽  
Ramon Michel Schreuder ◽  
Roger Fonollà ◽  
Thom Scheeve ◽  
Fons van der Sommen ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Multimodal Imaging ◽  
Narrow Band Imaging ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Optical Diagnosis ◽  
High Definition ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Optical diagnosis of colorectal polyps (CRPs) remains challenging. Imaging enhancement techniques such as narrow band imaging and blue light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high definition white light (HDWL) and BLI images, and compared it with the optical diagnosis of expert and novice endoscopists. Methods: The CADx characterized CRPs by exploiting artificial neural networks. Six experts and thirteen novices optically diagnosed 60 CRPs based on intuition. After a washout period of four weeks, the same set of CRPs was permuted and optically diagnosed using BASIC (BLI Adenoma Serrated International Classification). Results: The CADx had a diagnostic accuracy of 88.3% using HDWL images and 86.7% using BLI images. The overall diagnostic accuracy, combining HDWL and BLI (multimodal imaging), was 95.0% and significantly higher compared to experts (81.7%, p=0.031) and novices (66.5%, p<0.001). Sensitivity (95.6% vs. 61.1% and 55.4%) was also higher for CADx, while specificity was higher for experts compared to CADx and novices (94.1% vs 93.3% and 92.1%). For endoscopists, diagnostic accuracy did not increase using BASIC, neither for experts (Intuition 79.5% vs BASIC 81.7%, p=0.140) nor for novices (Intuition 66.7% vs BASIC 66.5%, p=0.953). Conclusion: The CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of CRPs. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of the CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared to intuitive optical diagnosis.

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