Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/5368
Title: | The classification of eye diseases from fundus images based on CNN and pretrained models | Authors: | Benbakreti, Samir Benbakreti, Soumia Özkaya, Umut |
Keywords: | Eye diseases classification retinal fundus images deep learning pretrained models SGDM Impairment |
Publisher: | Czech Technical Univ Prague | Abstract: | . Visual impairment affects more than a billion people worldwide due to insufficient care or inadequate vision screening. Computer-aided diagnosis using deep neural networks is a promising approach, it can analyse and process retinal fundus images, providing valuable reference data for doctors in clinical diagnosis or screening. This study aims to achieve an accurate classification of fundus images, including images of healthy patients as well as those with diabetic retinopathy, cataracts, and glaucoma, using a convolutional neural network (CNN) architecture and several pretrained models (AlexNet, GoogleNet, ResNet18, ResNet50, YOLOv3, and VGG 19). To enhance the training process, a mirror effect technique was applied to augment the volume of data. The experimental study resulted in very satisfactory outcomes, with the GoogleNet model paired with the SGDM optimiser achieving the highest accuracy (92.7 %). | URI: | https://doi.org/10.14311/AP.2024.64.0001 https://hdl.handle.net/20.500.13091/5368 |
ISSN: | 1805-2363 |
Appears in Collections: | WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
Show full item record
CORE Recommender
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.