Diabetic Retinal Tomographical Image Classification using Convolutionnal Neural Network
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Diabetic Retinal Tomographical Image Classification using Convolutionnal Neural Network |
| Type de publication | Conference Paper |
| Year of Publication | 2018 |
| Auteurs | Safarjalani R, Sidibe D, Ainouz S, Shahin A, Meriaudeau F |
| Conference Name | 2018 INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEM (ICIAS 2018) / WORLD ENGINEERING, SCIENCE & TECHNOLOGY CONGRESS (ESTCON) |
| Publisher | Univ Teknologi Petronas, Elect & Elect Engn Dept |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5386-7269-3 |
| Résumé | 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%. |