relation: https://khub.utp.edu.my/scholars/12929/
title: Performance Evaluation of Convolutions and Atrous Convolutions in Deep Networks for Retinal Disease Segmentation on Optical Coherence Tomography Volumes
creator: Alsaih, K.
creator: Yusoff, M.Z.
creator: Tang, T.B.
creator: Faye, I.
creator: Meriaudeau, F.
description: The deterioration of the retina center could be the main reason for vision loss. Older people usually ranging from 50 years and above are exposed to age-related macular degeneration (AMD) disease that strikes the retina. The lack of human expertise to interpret the complexity in diagnosing diseases leads to the importance of developing an accurate method to detect and localize the targeted infection. Approaching the performance of ophthalmologists is the consistent main challenge in retinal disease segmentation. Artificial intelligence techniques have shown enormous achievement in various tasks in computer vision. This paper depicts an automated end-to-end deep neural network for retinal disease segmentation on optical coherence tomography (OCT) scans. The work proposed in this study shows the performance difference between convolution operations and atrous convolution operations. Three deep semantic segmentation architectures, namely U-net, Segnet, and Deeplabv3+, have been considered to evaluate the performance of varying convolution operations. Empirical outcomes show a competitive performance to the human level, with an average dice score of 0.73 for retinal diseases. © 2020 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2020
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Alsaih, K. and Yusoff, M.Z. and Tang, T.B. and Faye, I. and Meriaudeau, F.  (2020) Performance Evaluation of Convolutions and Atrous Convolutions in Deep Networks for Retinal Disease Segmentation on Optical Coherence Tomography Volumes.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091044354&doi=10.1109%2fEMBC44109.2020.9175639&partnerID=40&md5=878a3ca1a3e61ab31a5cbb3c2a56b9c0
relation: 10.1109/EMBC44109.2020.9175639
identifier: 10.1109/EMBC44109.2020.9175639