Development of a deep learning-based early Alzheimer’s disease screening system using MRI for the Thai healthcare context
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Abstract
Thailand’s transition into an aging society has increased the prevalence of Alzheimer’s disease, making early-stage screening essential for improving patient outcomes. This paper proposes a deep learning-based screening system for early detection of Alzheimer’s disease using magnetic resonance imaging (MRI), tailored to the context of the Thai public healthcare system. A total of 6,400 MRI images has been used and classified into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Data preprocessing and augmentation techniques have been applied to enhance model performance. Four models have been evaluated, including the proposed Custom CNN, MobileNetV2, ResNet50, and VGG16. The results show that the proposed Custom CNN achieves the highest accuracy of 97.12%, with strong generalization and stable training performance. The model has also effectively reduced misclassification in early-stage categories. The proposed system demonstrates strong potential as a practical screening tool for early Alzheimer’s detection in Thailand, particularly in primary healthcare settings.
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