TY  - CONF
AV  - none
N2  - 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.
N1  - 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
KW  - Eye protection; Neural networks; Ophthalmology; Optical tomography
KW  -  Automatic classification; Automatic method; Classification accuracy; Convolutional neural network; Diabetic retinopathy; Retinal imaging; Sensitivity and specificity; Training data
KW  -  Image classification
ID  - scholars9635
TI  - Diabetic Retinal Tomographical Image Classification Using Convolutionnal Neural Network
Y1  - 2018///
SN  - 9781538672693
PB  - Institute of Electrical and Electronics Engineers Inc.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059782662&doi=10.1109%2fICIAS.2018.8540616&partnerID=40&md5=d529f81d40250e0240e61c76d61242a0
A1  - Safarjalani, R.
A1  - Sidibe, D.
A1  - Ainouz, S.
A1  - Shahin, A.
A1  - Meriaudeau, F.
ER  -