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صفحه اصلی / دومین کنفرانس ملی عصر انفجار تکنولوژی؛ هوش مصنوعی، تحولی در صنعت، تجارت و زنجیره تامین و دومین کنفرانس ملی علم داده در کاربردهای مهندسی

Early Alzheimer’s Detection with MRI-Based Deep Convolutional Neural Networks and Transfer Learning

نویسندگان :

Tabasom Musavi¹ M. J. Tarokh²

1- دانشگاه صنعتی خواجه نصیرالدین طوسی 2- دانشگاه صنعتی خواجه نصیرالدین طوسی

کلمات کلیدی :

Alzheimer’s disease،transfer learning, MRI،convolutional neural networks

چکیده :

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that lacks a definitive cure and imposes a growing burden on healthcare systems. Early and accurate detection is essential for managing the disease and improving patient outcomes. In this study, we investigate the effectiveness of two pre-trained convolutional neural networks, InceptionV3 and ResNet50, for classifying AD stages using T1-weighted MRI scans. Leveraging transfer learning, both models were fine-tuned on an augmented dataset consisting of four classes: no dementia, very mild, mild, and moderate dementia. Comprehensive preprocessing techniques were employed to enhance image quality and reduce noise. The results demonstrate that both InceptionV3 and ResNet50 achieve high accuracy in multi-class classification, highlighting their potential in assisting early AD diagnosis. While InceptionV3 showed strong feature extraction capabilities, ResNet50 offered a balance between performance and computational efficiency. These findings suggest that CNN-based models can provide scalable, reliable tools for clinical decision support in neurodegenerative disease diagnostics.