# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
###### Abstract  
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. d 202524 March 202517 April 202524 April 202524 March  
common symptoms include emotional volatility, linguistic difficulties, social withdrawal, and decreased motivation [1]. Currently, over 55 million people worldwide are living with dementia, and nearly 10 million new cases are diagnosed annually. It is the seventh leading cause of death and a major contributor to disability among the elderly globally [2]. There is currently no known cure for dementia, and the available treatment options are most effective if administered early, before the onset of symptoms [1; 3]. Unfortunately, most patients begin therapy when their daily functioning is already significantly impaired, at which point, treatment often does not yield optimal outcomes [4]. Alzheimer's disease (AD) accounts for 60% to 70% of all dementia cases worldwide, affecting roughly one in ten individuals over 65 and nearly a third of those over 85 [2]. Research suggests that AD progresses silently for decades before noticeable clinical symptoms emerge, with typical brain atrophy patterns appearing first in the medial temporal lobes [1; 5; 6]. Some individuals with these early signs develop mild cognitive impairment (MCI), an intermediate stage between normal aging-related cognitive decline and AD dementia, where memory deficits exceed those present in cognitively normal (CN) individuals but still remain insufficiently severe to disrupt daily functioning [7]. AD poses a significant diagnostic challenge due to its diverse clinical presentation across patients [8]. Early and accurate detection, particularly at the MCI stage, is essential for timely intervention, effective symptom management, and improved treatment outcomes [7; 9]. Standard diagnostic methods for dementia typically include clinical evaluations, neuropsychological assessments, cognitive testing, cerebrospinal fluid (CSF) and blood biomarker analysis, and neuroimaging [10]. While these approaches provide valuable insights, they are often expensive, invasive, time-consuming, and dependent on highly skilled specialists [11]. Magnetic resonance imaging (MRI), particularly T1-weighted structural scans, plays a pivotal role in diagnosing and monitoring AD, MCI, and other dementias. Its high spatial resolution makes it the preferred method for detecting early neurodegenerative changes such as hippocampal atrophy and cortical thinning, two of the better-established structural biomarkers of dementia progression [12]. Hippocampal atrophy is widely recognized as a hallmark of early AD pathology, often preceding the onset of cognitive symptoms [13].