Early diagnosis of Alzheimer’s Disease using hybrid CNN-Transformer models with Grad-CAM interpretability
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gumushane University
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Detecting Alzheimer's Disease (AD) at an early stage is vital because it enables prompt treatment and intervention, which can help slow disease progression and enhance patient prognosis. Given the increasing prevalence of AD globally, with an estimated 50 million people currently living with the condition and projected to triple by 2050, the development of accurate and efficient diagnostic tools is paramount. In this study, a novel architecture for the early diagnosis of AD by combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) with traditional Machine Learning (ML) algorithms was proposed. Utilizing MRI images as input, CNNs/ViTs serve as feature extractors, while demographic data is integrated to enhance diagnostic accuracy. Through extensive experimentation, our proposed model, which utilizes a CNN backbone optimized for MRI analysis as a feature extractor and LGBM as the classifier, achieved superior accuracy, reaching up to 96.83%. Statistical validation through confidence intervals and McNemar's test further demonstrated the robustness and significant performance improvements of the proposed model compared to baseline methods. This study employs eXplainable Al techniques to visualize critical regions in MRI images that influence the model's diagnostic decisions, promoting clinical transparency and trust in Al-assisted early diagnosis of AD. The novelty of this study lies in integrating deep feature extractors (CNNs/ViTs) with traditional ML classifiers, supported by interpretability through Grad-CAM and statistical validation, offering a transparent and accurate framework for early diagnosis of AD. © 2025, GUFBD/GUJS. All rights reserved.
Açıklama
Anahtar Kelimeler
Alzheimer's disease, Convolutional neural network, Dementia, Explainable AI, Magnetic resonance imaging, Vision transformer
Kaynak
Gumushane Universitesi Fen Bilimleri Dergisi
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
15
Sayı
3
