eprintid: 17418
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/18
datestamp: 2023-12-19 03:23:48
lastmod: 2023-12-19 03:23:48
status_changed: 2023-12-19 03:08:01
type: article
metadata_visibility: show
creators_name: Eu, C.Y.
creators_name: Tang, T.B.
creators_name: Lu, C.-K.
title: Automatic Polyp Segmentation in Colonoscopy Images Using Single Network Model: SegNet
ispublished: pub
keywords: Computer aided diagnosis; Convolution; Deep neural networks; Diseases; Endoscopy; Semantic Segmentation; Semantics, Colonoscopy; Colorectal cancer; Computer aided tools; Convolutional neural network; Dice coefficient; Lower resolution; Network models; Polyp segmentation; Single-networks; Survival probabilities, Convolutional neural networks
note: cited By 2; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
abstract: Colorectal cancer is the third most common diagnosed cancer worldwide. Early detection and removal of adenoma during the colonoscopy examination may increase the survival probability. A novel computer-aided tool for automated polyp segmentation in colonoscopy images is described in this work. SegNet, a deep convolutional neural networks has been chosen to map low resolution features with the input resolution for automated pixel-wise semantic polyp segmentation. Publicly available databases, CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB were used to train and to test the model. The outcome demonstrated the proposed method is feasible as it attains an average of 81.78, 92.35 for mean intersection over union, and dice coefficient, respectively for testing on a combination of the aforementioned datasets. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
date: 2022
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142681336&doi=10.1007%2f978-981-16-2183-3_69&partnerID=40&md5=d8ed6ddb147e85919e06dcd0f26a8701
id_number: 10.1007/978-981-16-2183-3₆₉
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 758
pagerange: 717-723
refereed: TRUE
isbn: 9789811621826
issn: 18761100
citation:   Eu, C.Y. and Tang, T.B. and Lu, C.-K.  (2022) Automatic Polyp Segmentation in Colonoscopy Images Using Single Network Model: SegNet.  Lecture Notes in Electrical Engineering, 758.  pp. 717-723.  ISSN 18761100