TY  - JOUR
SN  - 18761100
SP  - 717
UR  - 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
A1  - Eu, C.Y.
A1  - Tang, T.B.
A1  - Lu, C.-K.
PB  - Springer Science and Business Media Deutschland GmbH
TI  - Automatic Polyp Segmentation in Colonoscopy Images Using Single Network Model: SegNet
JF  - Lecture Notes in Electrical Engineering
EP  - 723
N1  - 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
ID  - scholars17418
Y1  - 2022///
N2  - 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.
KW  - Computer aided diagnosis; Convolution; Deep neural networks; Diseases; Endoscopy; Semantic Segmentation; Semantics
KW  -  Colonoscopy; Colorectal cancer; Computer aided tools; Convolutional neural network; Dice coefficient; Lower resolution; Network models; Polyp segmentation; Single-networks; Survival probabilities
KW  -  Convolutional neural networks
AV  - none
VL  - 758
ER  -