relation: https://khub.utp.edu.my/scholars/14585/
title: Automatic polyp segmentation in colonoscopy images using a modified deep convolutional encoder-decoder architecture
creator: Eu, C.Y.
creator: Tang, T.B.
creator: Lin, C.-H.
creator: Lee, L.H.
creator: Lu, C.-K.
description: Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the pre-ferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolu-tional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 � 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06, 94.55, 97.56, 97.48, 92.3, and 95.99, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clini-cians. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
publisher: MDPI AG
date: 2021
type: Article
type: PeerReviewed
identifier:   Eu, C.Y. and Tang, T.B. and Lin, C.-H. and Lee, L.H. and Lu, C.-K.  (2021) Automatic polyp segmentation in colonoscopy images using a modified deep convolutional encoder-decoder architecture.  Sensors, 21 (16).   ISSN 14248220     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113262692&doi=10.3390%2fs21165630&partnerID=40&md5=381137f2a3555d55bd31c054d3653e9f
relation: 10.3390/s21165630
identifier: 10.3390/s21165630