Détail du document
Identifiant
oai:arXiv.org:2503.11511
Sujet
Electrical Engineering and Systems... Computer Science - Artificial Inte... Computer Science - Computer Vision...Auteur
Kesu, Siva Manohar Reddy Sinha, Neelam Ramasangu, Hariharan Issac, Thomas GregorCatégorie
Computer Science
Année
2025
Date de référencement
19/03/2025
Mots clés
oct swin transnetoct computer science transformer alzheimer disease images accuracy retinalRésumé
Retinal optical coherence tomography (OCT) images are the biomarkers for neurodegenerative diseases, which are rising in prevalence.
Early detection of Alzheimer's disease using retinal OCT is a primary challenging task.
This work utilizes advanced deep learning techniques to classify retinal OCT images of subjects with Alzheimer's disease (AD) and healthy controls (CO).
The goal is to enhance diagnostic capabilities through efficient image analysis.
In the proposed model, Raw OCT images have been preprocessed with ImageJ and given to various deep-learning models to evaluate the accuracy.
The best classification architecture is TransNetOCT, which has an average accuracy of 98.18% for input OCT images and 98.91% for segmented OCT images for five-fold cross-validation compared to other models, and the Swin Transformer model has achieved an accuracy of 93.54%.
The evaluation accuracy metric demonstrated TransNetOCT and Swin transformer models capability to classify AD and CO subjects reliably, contributing to the potential for improved diagnostic processes in clinical settings.
;Comment: 18 pages, 25 figures
Kesu, Siva Manohar Reddy,Sinha, Neelam,Ramasangu, Hariharan,Issac, Thomas Gregor, 2025, Alzheimer's Disease Classification Using Retinal OCT: TransnetOCT and Swin Transformer Models
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