Early diagnosis of Alzheimer’s Disease using hybrid CNN-Transformer models with Grad-CAM interpretability

Year 2025, Volume: 15 Issue: 3, 829 - 853, 15.09.2025

Pakize Erdoğmuş , Abdullah Talha Kabakuş

https://doi.org/10.17714/gumusfenbil.1714884

Abstract

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 AI techniques to visualize critical regions in MRI images that influence the model’s diagnostic decisions, promoting clinical transparency and trust in AI-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.

Keywords

Alzheimer’s disease , Convolutional neural network , Dementia , Explainable AI , Magnetic resonance imaging , Vision transformer

Ethical Statement

This study utilized only de-identified, publicly available data from OASIS-2, which was originally collected with full ethical approval by Washington University (Marcus et al., 2010), including participant consent for public sharing of de-identified data. All methods were performed in accordance with relevant data-use agreements.

Thanks

We would like to thank the maintainers of the OASIS-2 for publicly sharing their dataset as a contribution to the research field.

Grad-CAM yorumlanabilirliği ile hibrit CNN-Transformer modeller kullanılarak Alzheimer Hastalığının erken tanısı

Abstract

Alzheimer Hastalığını (AH) erken evrede tespit etmek hızlı tedavi ve müdahaleye olanak sağlaması açısından çok önemlidir. Bu sayede hastalığın ilerlemesi yavaşlatılabilir ve hasta prognozu iyileştirilebilir. Dünya genelinde AH’nin artan yaygınlığı göz önüne alındığında — şu anda yaklaşık 50 milyon kişinin bu hastalıkla yaşadığı ve bu sayının 2050 yılına kadar üç katına çıkacağı öngörüldüğünde — doğru ve etkili tanı araçlarının geliştirilmesi kritik hale gelmiştir. Bu çalışmada, Konvolüsyonel Sinir Ağları (CNN’ler) veya Görüntü Dönüştürücüler (ViT’ler) ile geleneksel Makine Öğrenmesi (ML) algoritmalarını birleştirerek Alzheimer hastalığının erken tanısına yönelik özgün bir mimari sunulmaktadır. Girdi olarak MRI (Manyetik Rezonans Görüntüleme) görüntülerini kullanan CNN/ViT modelleri özellik çıkarıcı olarak işlev görmekte ve tanı doğruluğunu artırmak amacıyla demografik verilerle birleştirilmektedir. Gerçekleştirilen kapsamlı deneyler sonucunda, MRI analizi için optimize edilmiş bir CNN tabanlı özellik çıkarıcı ile LGBM sınıflandırıcısının kullanıldığı önerilen modelimiz %96,83’e varan doğruluk oranı ile üstün performans sergilemiştir. Güven aralıkları ve McNemar testi yoluyla yapılan istatistiksel doğrulamalar, önerilen modelin temel yöntemlere kıyasla sağlamlığını ve anlamlı performans iyileştirmelerini desteklemiştir. Bu çalışma, Açıklanabilir Yapay Zeka tekniklerini kullanarak modelin tanısal kararlarını etkileyen MRG görüntülerindeki kritik bölgeler görselleştirilmiş ve böylece yapay zeka destekli erken teşhis süreçlerinde klinik şeffaflık ve güven teşvik edilmiştir. Bu çalışmanın özgünlüğü, derin özellik çıkarıcıların (CNN’ler/ViT’ler) geleneksel ML sınıflandırıcılarıyla bütünleştirilmesinde yatmaktadır. Bu yapı, Grad-CAM tabanlı yorumlanabilirlik ve istatistiksel doğrulama ile desteklenerek, erken AH tanısı için şeffaf ve yüksek doğrulukta bir çerçeve sunmaktadır.

Keywords

Alzheimer hastalığı , Evrişimsel sinir ağı , Demans , Açıklanabilir yapay zeka , Manyetik rezonans görüntüleme , Görüntü dönüştürücüsü

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