An embedded recurrent neural network-based model for endoscopic semantic segmentation

Haithami, M. and Ahmed, A. and Liao, I.Y. and Jalab, Hamid A. (2021) An embedded recurrent neural network-based model for endoscopic semantic segmentation. In: 3rd International Workshop and Challenge on Computer Vision in Endoscopy, EndoCV 2021, 13 April 2021, Nice.

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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).

Item Type: Conference or Workshop Item (Paper)
Funders: None
Uncontrolled Keywords: Embedded RNN; GRU; Polyp Segmentation; SegNet
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms Zaharah Ramly
Date Deposited: 11 Oct 2023 04:24
Last Modified: 11 Oct 2023 04:24

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