eprintid: 9840
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/98/40
datestamp: 2023-11-09 16:36:29
lastmod: 2023-11-09 16:36:29
status_changed: 2023-11-09 16:29:57
type: conference_item
metadata_visibility: show
creators_name: Chan, G.C.Y.
creators_name: Kamble, R.
creators_name: Muller, H.
creators_name: Shah, S.A.A.
creators_name: Tang, T.B.
creators_name: Meriaudeau, F.
title: Fusing Results of Several Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema in Optical Coherence Tomography
ispublished: pub
keywords: algorithm; artificial neural network; diabetic complication; diagnostic imaging; human; macular edema; optical coherence tomography; principal component analysis, Algorithms; Deep Learning; Diabetes Complications; Humans; Macular Edema; Neural Networks (Computer); Principal Component Analysis; Tomography, Optical Coherence
note: cited By 22; Conference of 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 ; Conference Date: 18 July 2018 Through 21 July 2018; Conference Code:141674
abstract: Diabetic Macular Edema (DME) is a severe eye disease that can lead to irreversible blindness if it is left untreated. DME diagnosis still relies on manual evaluation from opthalmologists, thus the process is time consuming and diagnosis may be subjective. This paper presents two novel DME detection frameworks: (1) combining features from three pre-trained Convolutional Neural Networks: AlexNet, VggNet and GoogleNet and performing feature space reduction using Principal Component Analysis and (2) a majority voting scheme based on a plurality rule between classifications from AlexNet, VggNet and GoogleNet. Experiments were conducted using Optical Coherence Tomography datasets retrieved from the Singapore Eye Research Institute and the Chinese University Hong Kong. The results are evaluated using a Leave-Two-Patients-Out Cross Validation at the volume level. This method improves DME classification with an accuracy of 93.75, which is similar to the best algorithms so far on the same data sets. © 2018 IEEE.
date: 2018
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056618510&doi=10.1109%2fEMBC.2018.8512371&partnerID=40&md5=3817d4554b6334f230f9b2d70b8ecb5c
id_number: 10.1109/EMBC.2018.8512371
full_text_status: none
publication: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
volume: 2018-J
pagerange: 670-673
refereed: TRUE
isbn: 9781538636466
issn: 1557170X
citation:   Chan, G.C.Y. and Kamble, R. and Muller, H. and Shah, S.A.A. and Tang, T.B. and Meriaudeau, F.  (2018) Fusing Results of Several Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema in Optical Coherence Tomography.  In: UNSPECIFIED.