Improving Alzheimer's Diagnosis using Vision Transformers and Transfer Learning

被引：0

作者：

Zaabi, Marwa ^{[1
]}

Ibn Khedher, Mohamed ^{[2
]}

El-Yacoubi, Mounim A. ^{[3
]}

机构：

[1] Sfax Univ, CEM Lab, ENIS, Sfax, Tunisia

[2] IRT SystemX, 2 Bd Thomas Gobert, F-91120 Palaiseau, France

[3] Telecom SudParis, Inst Polytech Paris, Samovar, 19 Pl Marguerite Perey, F-91120 Palaiseau, France

来源：

2024 16TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTION, HSI 2024 | 2024年

关键词：

Alzheimer's disease; Vision Transformer; Transfer Learning; NEURAL-NETWORK; MRI;

D O I：

10.1109/HSI61632.2024.10613527

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Alzheimer's disease is a neurodegenerative disorder defined by memory loss and primarily affects older individuals. Currently, there is no definitive cure available. Although medications are accessible, they only serve to slow the progression of the disease. In this paper, we propose the use of Vision Transformers and Transfer Learning for Alzheimer's classification. Our approach leverages the temporal aspect of the transformer to model the correlation between different image patches. Transfer learning enables us to mitigate the issue of insufficient available data. Our method has been validated on the OASIS dataset, which consists of 250 brain scans. The results demonstrate that transfer learning with Transformer models surpasses the performance of transfer learning with CNN models by 4% and exceeds traditional CNN models without transfer learning by 8%. Two types of Transformers were tested: ViT-B16 and ViT-B32. The results are comparable, with ViT-B32 outperforming ViT-B16 by 1%.

引用

页数：6