# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
###### Abstract  
Alzheimer's disease (AD), the most common form of dementia and neurological disorder, affects a significant number of elderly people worldwide. The main objective of this study was to develop an effective method for quickly diagnosing healthy individuals (CN) before they progress to mild cognitive impairment (MCI). Moreover, this study presents a unique approach to decomposing AD into stages using machine-learning architectures with the help of tensor-based morphometric image analysis. The proposed model, which uses a neural network built on the Xception architecture, was thoroughly assessed by comparing it with the most recent convolutional neural network (CNN) models described in the literature. The proposed method outperformed the other models in terms of performance, achieving an impressive average classification accuracy of 95.81% using the dataset. It also had very high sensitivity, specificity, accuracy, and F1 scores, with average scores of 95.41%, 97.92%, 95.01%, and 95.21%, respectively. In addition, it showed a superior classification ability compared to alternative methods, especially for MCI estimation, as evidenced by a mean area under the ROC curve (AUC) of 0.97. Our study demonstrated the effectiveness of deep-learning-based morphometric analysis using brain images for early AD diagnosis. Alzheimer's disease; transfer learning; image classification; deep learning; tensor-based morphometry +
Footnote †: journal: Medical  
These areas show a reduction in neuronal and synaptic density, contributing to cognitive decline. Magnetic resonance imaging (MRI) can visualize the atrophy of the hippocampus and other brain regions associated with memory processing and executive functions [6]. Figure 1 presents the prominent brain atrophy that is evident in individuals diagnosed with MCI and AD [7]. However, this phenomenon was not observed in healthy individuals. In Figure 1, the dimensions of the hippocampal tissue in MCI subjects, as highlighted with the red arrow, are smaller than those of cognitively normal (CN) subjects and further decrease in AD subjects. The magnitude of the ventricles undergoes a significant transformation, with an increase in size as the disease advances, as depicted with the red stars. Also, the decrease in the amount of gray matter in the cerebral cortex can be observed in magnified images of CN subjects compared to AD subjects. MRI is a medical imaging modality that enables the production of high-resolution images that can visualize the differences between brain tissues [8]. Morphometric analysis is the process of obtaining quantitative data by evaluating and analyzing the geometric features of objects by processing MRI images. Through the amalgamation of MRI and morphometric analysis approaches, one can appraise the volume, morphology, and additional geometric characteristics of cerebral regions. In this way, its extensive utilization encompasses the diagnosis of cerebral disorders and the formulation of treatment strategies. Voxel-based morphometry (VBM) is the most widely known morphometric method. VBM calculates the density or concentration of gray matter in a voxel-wise manner [9]. Other morphometric methods, deformation-based morphometry (DBM) and tensor-based morphometry (TBM) use similar measurement techniques to characterize the differences in brain shape. DBM and TBM images are recorded in a common reference space and analyzed using the parameters of the deformation fields or measurements derived from them [10]. Surface-based morphometry (SBM) is another morphometric method that is used to analyze the surface properties of the cerebral cortex. The cerebral cortex can be modeled with a spherical model, and the features (thickness, fold depth, and surface area) in this model can be measured statistically [11]. Among the various methodologies available for neuroimaging analyses and clinical trials, TBM stands out as a highly reliable and objective measure with a significant capacity for high-throughput imaging [12].