eprintid: 9635
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/96/35
datestamp: 2023-11-09 16:36:17
lastmod: 2023-11-09 16:36:17
status_changed: 2023-11-09 16:29:27
type: conference_item
metadata_visibility: show
creators_name: Safarjalani, R.
creators_name: Sidibe, D.
creators_name: Ainouz, S.
creators_name: Shahin, A.
creators_name: Meriaudeau, F.
title: Diabetic Retinal Tomographical Image Classification Using Convolutionnal Neural Network
ispublished: pub
keywords: Eye protection; Neural networks; Ophthalmology; Optical tomography, Automatic classification; Automatic method; Classification accuracy; Convolutional neural network; Diabetic retinopathy; Retinal imaging; Sensitivity and specificity; Training data, Image classification
note: cited By 0; Conference of 7th International Conference on Intelligent and Advanced System, ICIAS 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:143005
abstract: 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.
date: 2018
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059782662&doi=10.1109%2fICIAS.2018.8540616&partnerID=40&md5=d529f81d40250e0240e61c76d61242a0
id_number: 10.1109/ICIAS.2018.8540616
full_text_status: none
publication: International Conference on Intelligent and Advanced System, ICIAS 2018
refereed: TRUE
isbn: 9781538672693
citation:   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.