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Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks
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DOI：10.3390/s23249717.png)
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摘要
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The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures.
Keyword:
machine learning
artificial intelligence
computer vision algorithms
convolutional neural networks
medical image analysis
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IF:
3.5
论文数:
7.2W
被引数:
20.9W
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