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Abstract
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.
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s23249717
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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SENSORS
no. 23,
ISSN: 1424-8220 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Brzeski A., Dziubich T., Krawczyk H.: Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks// SENSORS -Vol. 23,iss. 24 (2023), s.9717-9739
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s23249717
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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