A comprehensive review of deep learning in colon cancer
Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …
attracted attention with its achievements in progressing medical image analysis …
Deep learning for medical image processing: Overview, challenges and the future
MI Razzak, S Naz, A Zaib - … in BioApps: Automation of decision making, 2018 - Springer
The health care sector is totally different from any other industry. It is a high priority sector
and consumers expect the highest level of care and services regardless of cost. The health …
and consumers expect the highest level of care and services regardless of cost. The health …
Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer
This paper investigates a deep learning method in image classification for the detection of
colorectal cancer with ResNet architecture. The exceptional performance of a deep learning …
colorectal cancer with ResNet architecture. The exceptional performance of a deep learning …
Automatic polyp segmentation via multi-scale subtraction network
More than 90% of colorectal cancer is gradually transformed from colorectal polyps. In
clinical practice, precise polyp segmentation provides important information in the early …
clinical practice, precise polyp segmentation provides important information in the early …
Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives
Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
Camouflaged object detection via context-aware cross-level fusion
Camouflaged object detection (COD) aims to identify the objects that conceal themselves in
natural scenes. Accurate COD suffers from a number of challenges associated with low …
natural scenes. Accurate COD suffers from a number of challenges associated with low …
Convolutional neural networks for medical image analysis: Full training or fine tuning?
N Tajbakhsh, JY Shin, SR Gurudu… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
Training a deep convolutional neural network (CNN) from scratch is difficult because it
requires a large amount of labeled training data and a great deal of expertise to ensure …
requires a large amount of labeled training data and a great deal of expertise to ensure …
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning
Ensemble models achieve high accuracy by combining a number of base estimators and
can increase the reliability of machine learning compared to a single estimator. Additionally …
can increase the reliability of machine learning compared to a single estimator. Additionally …
Automated polyp detection in colonoscopy videos using shape and context information
N Tajbakhsh, SR Gurudu, J Liang - IEEE transactions on …, 2015 - ieeexplore.ieee.org
This paper presents the culmination of our research in designing a system for computer-
aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid …
aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid …
[HTML][HTML] Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy
Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been
identified, and solutions are critically needed. Hence, the development of a real-time robust …
identified, and solutions are critically needed. Hence, the development of a real-time robust …