Depot Institutionnel de l'UMBB >
Publications Scientifiques >
Communications Internationales >
Veuillez utiliser cette adresse pour citer ce document :
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/14122
|
Titre: | Progressive Deep Transfer Learning for Accurate Glaucoma Detection in Medical Imaging |
Auteur(s): | Yakoub, Assia Gaceb, Djamel Touazi, Fayçal Bourahla, Nourelhouda |
Mots-clés: | Glaucoma detection Computer-aided diagnosis system in medical imaging Deep transfer learning Computer vision Artificial intelligence |
Date de publication: | 2024 |
Editeur: | Institute of Electrical and Electronics Engineers |
Collection/Numéro: | 2024 8th International Conference on Image and Signal Processing and their Applications (ISPA), Biskra, Algeria, 2024;pp. 1-7 |
Résumé: | Glaucoma leads to permanent vision disability by damaging the optic nerve, which transmits visual images to the brain. The fact that glaucoma doesn't exhibit any symptoms as it progresses and can't be halted in later stages makes early diagnosis critical. Although various deep learning models have been applied to detect glaucoma from digital fundus images, the scarcity of labeled data has limited their generalization performance, along with their high computational complexity and specialized hardware requirements. In this study, a progressive transfer learning with preprocessing techniques is proposed for the early detection of glaucoma in fundus images. The performance of this approach is compared against transfer learning and convolutional neural networks using three benchmark datasets: Cataract, Glaucoma and Origa. The experimental results demonstrate that reusing pre-trained models from ImageNet and applying them to a database containing the same disease leads to improved performance, compared to using databases with different diseases in progressive transfer learning. Additionally, applying preprocessing techniques to the databases further enhances the results. |
URI/URL: | 10.1109/ISPA59904.2024.10536857 https://ieeexplore.ieee.org/document/10536857 http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/14122 |
Collection(s) : | Communications Internationales
|
Fichier(s) constituant ce document :
Il n'y a pas de fichiers associés à ce document.
|
Tous les documents dans DSpace sont protégés par copyright, avec tous droits réservés.
|