# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
###### Abstract
Cortical thinning, especially in the temporoparietal and medial temporal regions, correlates with both disease severity and the risk of conversion from MCI to AD [14]. These changes offer objective, quantitative metrics that help differentiate between CN individuals, MCI patients, and those with AD. Recent advances in artificial intelligence (AI) have further enhanced the diagnostic value of neuroimaging and the utility of its associated biomarkers, leading to the development of robust predictive frameworks for dementia [15]. AI-based tools can detect subtle, spatially distributed atrophy patterns, such as microstructural changes in the hippocampal region and focal cortical thinning, often before cognitive decline becomes evident [16]. These biomarkers constitute precise and quantifiable measures of neurodegeneration, making them accurate key early indicators [17]. They not only support individualized disease monitoring and prognosis but also directly contribute to improved patient outcomes [18]. Recently, deep learning (DL) techniques, particularly convolutional neural networks (CNNs), have emerged as powerful tools for detecting and analyzing dementia-specific biomarkers [19]. By processing extensive radiological datasets, CNN-based models capture intricate neurodegeneration patterns, enabling accurate disease progression predictions [20; 21]. Their application extends beyond diagnosis, facilitating the development of personalized treatment strategies [22; 23]. Traditional machine learning (ML) algorithms such as support vector machines (SVMs), random forests, and boosting methods have long been used for dementia classification tasks. Variants of SVMs have previously been proposed to improve performance [24]. However, all these approaches generally rely on manually extracting regions of interest (ROIs) based on known MRI brain biomarkers, which is time-consuming and subjective given the absence of universally agreed-upon dementia biomarkers. As a result, conventional models may fail to capture the full complexity of AD presentation and are often limited by labor-intensive feature engineering and risk of overfitting [25]. DL offers a more scalable and data-driven alternative. Architectures such as CNNs, recurrent neural networks (RNNs), and autoencoders can learn discriminative features without manual input [24; 26]. Among these, CNNs have become the most widely adopted for AI-driven neuroimaging analysis due to their ability to exploit spatial hierarchies in image data, reduce complexity via weight sharing, and operate directly on image slices [27]. Notably, CNNs trained on 2D axial MRI slices have demonstrated strong generalization across scanners and protocols. Models such as pre-trained AlexNet and VGG-16 have achieved classification accuracies as high as 99.21% and 95.73%, respectively [24; 25]. Beyond standard CNNs, recent studies have incorporated mechanisms to improve the localization of informative brain regions. Adaptive hybrid networks such as those proposed by [PERSON] et al. [28], which integrate non-local and coordinate attention modules, have achieved classification accuracies of up to 98.53%. Similarly, [PERSON] et al. [29] developed a multimodal framework combining 3D attention with ResNet to improve diagnostic accuracy for both AD and MCI. Other works have integrated CNNs with transformer architectures and novel attention-based fusion strategies to model local and global contextual features better [25; 30]. To overcome the limitations of 2D models, such as the loss of inter-slice spatial continuity, researchers have begun to explore volumetric modeling using 3D CNNs. For example, [PERSON] et al. [31] introduced a 3D generative adversarial network with a three-round learning strategy, achieving 92.8% accuracy in binary classification tasks. [PERSON] et al. [29] extended this by integrating multimodal data into a 3D ResNet framework, outperforming single-modality models by 3-6%. [PERSON] et al.