relation: https://khub.utp.edu.my/scholars/17418/
title: Automatic Polyp Segmentation in Colonoscopy Images Using Single Network Model: SegNet
creator: Eu, C.Y.
creator: Tang, T.B.
creator: Lu, C.-K.
description: 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.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022
type: Article
type: PeerReviewed
identifier:   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     
relation: 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
relation: 10.1007/978-981-16-2183-3₆₉
identifier: 10.1007/978-981-16-2183-3₆₉