Machine and Deep Learning Approaches for Alzheimer's Disease Classification with EEG Signals and MRI Images
DOI:
https://doi.org/10.21015/vtse.v13i4.2239Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the main cause of dementia globally, making early and accurate diagnosis crucial for early and effective treatment. Traditional diagnostic approaches, such as MRI and EEG-based diagnosis, have limitations due to their high cost, lengthy execution times, and the need for specialized clinical expertise. Recent advances in machine learning (ML) and deep learning (DL) offer new prospects for automating and improving AD detection by extracting discriminative structures from multimodal biomedical data. This study proposes a smart system for Alzheimer’s disease classification based on the MRI images. Various deep learning models such as VGG16, InceptionV3, and ResNet50 were used for MRI-based classification. Various preprocessing, enhancement, and feature selection techniques were applied to enhance data quality and model performance. Experimental results show that VGG16 achieves the best accuracy of 98% on MRI images followed by the Inception 3 model. The RestNet50 model performed worst compared to these two models with respect to accuracy, F1 score and Recall. The proposed work shows the potential of using advanced learning algorithms to achieve robust and scalable early diagnosis of Alzheimer’s disease, thereby assisting clinicians in reducing misdiagnosis and improving patient treatment outcomes.
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