Computer-aided diagnosis system using only white-light endoscopy for the prediction of invasion depth in colorectal cancer

2020 ◽  
Author(s):  
Mamoru Tokunaga ◽  
Tomoaki Matsumura ◽  
Rino Nankinzan ◽  
Takuto Suzuki ◽  
Hirotaka Oura ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
White Light ◽  
Computer Aided Diagnosis ◽  
Invasion Depth ◽  
Diagnosis System ◽  
White Light Endoscopy ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-Aided Diagnosis System for Colorectal Cancer

2021 ◽  
Author(s):  
Eva-H. Dulf ◽  
Cristina I. Muresan ◽  
Teodora Mocan ◽  
Lucian Mocan
Keyword(s):  
Colorectal Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

2021 ◽  
Vol 69 ◽  
pp. 102914
Author(s):  
Raouia Mokni ◽  
Norhene Gargouri ◽  
Alima Damak ◽  
Dorra Sellami ◽  
Wiem Feki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Multimodal Fusion ◽  
Diagnosis System ◽  
Automatic Computer ◽  
Computer Aided ◽  
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.

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