relation: https://khub.utp.edu.my/scholars/9650/
title: Deep Features and Data Reduction for Classification of SD-OCT Images: Application to Diabetic Macular Edema
creator: Chan, G.C.Y.
creator: Shah, S.A.A.
creator: Tang, T.B.
creator: Lu, C.-K.
creator: Muller, H.
creator: Meriaudeau, F.
description: Diabetic Macular Edema (DME) is defined as the accumulation of extracellular fluids in the macular region of the eye, caused by Diabetic Retinopathy (DR) that will lead to irreversible vision loss if left untreated. This paper presents the use of a pre-trained Convolutional Neural Network (CNN) based model for the classification of Spectral Domain Optical Coherence Tomography (SD- OCT) images of Diabetic Macular Edema (DME) with feature reduction using Principal Component Analysis (PCA) and Bag of Words (BoW). The model is trained using SD-OCT dataset retrieved from the Singapore Eye Research Institute (SERI) and is evaluated using an 8-fold cross validation at the slide level and two patient leave out at the volume level. For the volume level, an accuracy of 96.88 is obtained for data that was preprocessed. © 2018 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Chan, G.C.Y. and Shah, S.A.A. and Tang, T.B. and Lu, C.-K. and Muller, H. and Meriaudeau, F.  (2018) Deep Features and Data Reduction for Classification of SD-OCT Images: Application to Diabetic Macular Edema.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059764801&doi=10.1109%2fICIAS.2018.8540579&partnerID=40&md5=d1e7f9f956e3de515b85897095ee7313
relation: 10.1109/ICIAS.2018.8540579
identifier: 10.1109/ICIAS.2018.8540579