1. Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks.
- Author
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Brzeski, Adam, Dziubich, Tomasz, and Krawczyk, Henryk
- Subjects
- *
CONVOLUTIONAL neural networks , *ARTIFICIAL neural networks , *IMAGE recognition (Computer vision) , *IMAGE processing , *COMPUTER vision - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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