ID: 3523068 COMPUTER-AIDED DIAGNOSIS OF COLORECTAL CANCER WITH DEEP SUBMUCOSAL INVASION USING NON-MAGNIFIED WHITE LIGHT ENDOSCOPIC IMAGES COMPARED WITH ENDOSCOPISTS

2021 ◽  
Vol 93 (6) ◽  
pp. AB196
Author(s):  
Takahito Takezawa ◽  
Zhe Guo ◽  
Daiki Nemoto ◽  
Shinichi Katsuki ◽  
Ryo Maemoto ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
White Light ◽  
Computer Aided Diagnosis ◽  
Submucosal Invasion ◽  
Endoscopic Images ◽  
Computer Aided ◽  
Aided Diagnosis

ID: 3523021 COMPUTER-AIDED DIAGNOSIS FOR COLORECTAL SERRATED LESIONS USING NON-MAGNIFIED WHITE LIGHT ENDOSCOPIC IMAGES

2021 ◽  
Vol 93 (6) ◽  
pp. AB206
Author(s):  
Yuki Nakajima ◽  
Zhe Guo ◽  
Daiki Nemoto ◽  
Boyuan Peng ◽  
Ruiyao Zhang ◽  
...  
Keyword(s):  
White Light ◽  
Computer Aided Diagnosis ◽  
Endoscopic Images ◽  
Computer Aided ◽  
Serrated Lesions ◽  
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.

Medical image analysis: computer-aided diagnosis of gastric cancer invasion on endoscopic images

2011 ◽  
Vol 26 (5) ◽  
pp. 1485-1489 ◽  
Author(s):  
Keisuke Kubota ◽  
Junko Kuroda ◽  
Masashi Yoshida ◽  
Keiichiro Ohta ◽  
Masaki Kitajima
Keyword(s):  
Gastric Cancer ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Cancer Invasion ◽  
Computer Aided Diagnosis ◽  
Endoscopic Images ◽  
Computer Aided ◽  
Analysis Computer ◽  
Aided Diagnosis

scholarly journals Polyp detection with computer-aided diagnosis in white light colonoscopy: comparison of three different methods

2019 ◽  
Vol 07 (02) ◽  
pp. E209-E215 ◽  
Author(s):  
Pedro Figueiredo ◽  
Isabel Figueiredo ◽  
Luís Pinto ◽  
Sunil Kumar ◽  
Yen-Hsi Tsai ◽  
...  
Keyword(s):  
White Light ◽  
Normal Mucosa ◽  
Computer Aided Diagnosis ◽  
Polyp Detection ◽  
Machine Learning Methods ◽  
Computer Aided ◽  
Mathematical Techniques ◽  
Binary Classifiers ◽  
Wireless Capsule ◽  
Aided Diagnosis

Abstract Background and study aims Detection of polyps during colonoscopy is essential for screening colorectal cancer and computer-aided-diagnosis (CAD) could be helpful for this objective. The goal of this study was to assess the efficacy of CAD in detection of polyps in video colonoscopy by using three methods we have proposed and applied for diagnosis of polyps in wireless capsule colonoscopy. Patients and methods Forty-two patients were included in the study, each one bearing one polyp. A dataset was generated with a total of 1680 polyp instances and 1360 frames of normal mucosa. We used three methods, that are all binary classifiers, labelling a frame as either containing a polyp or not. Two of the methods (Methods 1 and 2) are threshold-based and address the problem of polyp detection (i. e. separation between normal mucosa frames and polyp frames) and the problem of polyp localization (i. e. the ability to locate the polyp in a frame). The third method (Method 3) belongs to the class of machine learning methods and only addresses the polyp detection problem. The mathematical techniques underlying these three methods rely on appropriate fusion of information about the shape, color and texture content of the objects presented in the medical images. Results Regarding polyp localization, the best method is Method 1 with a sensitivity of 71.8 %. Comparing the performance of the three methods in the detection of polyps, independently of the precision in the location of the lesions, Method 3 stands out, achieving a sensitivity of 99.7 %, an accuracy of 91.1 %, and a specificity of 84.9 %. Conclusion CAD, using the three studied methods, showed good accuracy in the detection of polyps with white light colonoscopy.

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