relation: https://khub.utp.edu.my/scholars/9635/
title: Diabetic Retinal Tomographical Image Classification Using Convolutionnal Neural Network
creator: Safarjalani, R.
creator: Sidibe, D.
creator: Ainouz, S.
creator: Shahin, A.
creator: Meriaudeau, F.
description: Diabetic Macular Edema (DME) is the most common cause of permanent vision loss among people with diabetic retinopathy. However, early detection and treatment can reduce the risk of blindness. This paper presents an automatic method to detect DME and DR and oversteps the subjective manual evaluation of opthalmologists. Based on Convolutional Neural Network, a proposed end-to-end CNN model is presented and fully trained for the automatic classification of Optical Coherence Tomography (OCT) retinal imaging. The experiments over two datasets, provided by different institutions, have been evaluated by randomly shuffling and separating the training data along with test data. Using the proposed model, the experiment results showed a classification accuracy, sensitivity and specificity of 99.02. © 2018 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Safarjalani, R. and Sidibe, D. and Ainouz, S. and Shahin, A. and Meriaudeau, F.  (2018) Diabetic Retinal Tomographical Image Classification Using Convolutionnal Neural Network.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059782662&doi=10.1109%2fICIAS.2018.8540616&partnerID=40&md5=d529f81d40250e0240e61c76d61242a0
relation: 10.1109/ICIAS.2018.8540616
identifier: 10.1109/ICIAS.2018.8540616