Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification

This study explores the potential of using convolutional neural networks (CNNs) to diagnose dementia early and manage it in an individualized way. Segmented brain magnetic resonance imaging (MRI) images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database represented Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively normal (CN) subjects. These classes served to train, validate, and test CNN-based models. The first four models were developed entirely from scratch, and the other four employed transfer learning (TL). While both approaches demonstrated high classification accuracy (93.69% on average), TL-based models outperformed independently developed ones, achieving 97.64% accuracy compared with 89.75%. The CNN-based models yielded information about detected dementia type, diagnosis confidence level, and gradient-weighted class activation mapping (Grad-CAM)-generated heatmaps highlighting pathologically affected brain regions. These results indicate the high potential of CNN-based models for enhancing early dementia detection and differentiation and offer a promising basis for developing deep learning (DL)-based clinical decision support systems (CDSSs). Such systems could assist healthcare professionals in reducing dementia diagnosis time, optimizing patient-tailored management and treatment strategies, and improving the quality of life for individuals with dementia.