An embedded recurrent neural network-based model for endoscopic semantic segmentation
Document Type
Conference Item
Publication Date
1-1-2021
Abstract
Detecting cancers at their early stage would decrease mortality rate. For instance, detecting all polyps during colonoscopy would increase the chances of a better prognoses. However, endoscopists are facing difficulties due to the heavy workload of analyzing endoscopic images. Hence, assisting endoscopist while screening would decrease polyp miss rate. In this study, we propose a new deep learning segmentation model to segment polyps found in endoscopic images extracted during Colonoscopy screening. The propose model modifies SegNet architecture to embed Gated recurrent units (GRU) units within the convolution layers to collect contextual information. Therefore, both global and local information are extracted and propagated through the entire layers. This has led to better segmentation performance compared to that of using state of the art SegNet. Four experiments were conducted and the proposed model achieved a better intersection over union “IoU” by 1.36, 1.71, and 1.47 on validation sets and 0.24 on a test set, compared to the state of the art SegNet. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Keywords
Embedded RNN, GRU, Polyp Segmentation, SegNet
Divisions
fsktm
Funders
None
Publication Title
CEUR Workshop Proceedings
Volume
2886
Event Title
3rd International Workshop and Challenge on Computer Vision in Endoscopy, EndoCV 2021
Event Location
Nice
Event Dates
13 April 2021
Event Type
conference