# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
###### Abstract
Machine-learning (ML)-based systems have been successfully applied in various fields such as energy [13], robotics [14] health [15], and transportation [16]. These systems have demonstrated potential for assisting radiologists and physicians in the timely identification and categorization of AD via computer-aided diagnosis (CAD) systems [17]. Timely diagnosis and accurate analysis of brain atrophy are crucial, and the automated detection of brain atrophy can greatly contribute to these goals. Additionally, it can optimize radiologist efficiency by providing more accurate and efficient results. Various ML techniques, including feature extraction, deep networks, and transfer learning (TL), have been proposed  
Figure 1: MRI scans of (**a**) CN, (**b**) MCI, and (**c**) AD adapted with permission from [7], [PERSON], 2018.  
for the classification of AD and MCI [18; 19; 20; 21]. Deep learning (DL), consisting of artificial neurons, has demonstrated superior performance in handling complex classification tasks compared to traditional ML methods [22; 23]. CNNs, a specialized DL technique, have been widely utilized for AD diagnosis [24; 25]. The primary objective of this present research is to devise a highly effective approach for the timely detection of patients in the MCI phase prior to their progression toward the AD stage. Encouraged by the aforementioned findings, we aimed to add to the detection of AD by combining DL and TBM methods. This study introduced a novel method that utilizes cutting-edge architectures (Xception, VGG16, VGG19, and ResNet-50V2) to accurately detect AD and classify the different stages of dementia (MCI and AD). The proposed model, incorporating the Xception architecture-based deep dense block, was comprehensively evaluated via comparison with modern DL techniques. The main aim of this article is to develop an effective method for early diagnosis of patients in the MCI stage before they progress to the AD stage. Existing studies on Alzheimer's diagnosis are limited, mostly focusing on traditional machine-learning-based methods for feature extraction from raw/semi-processed MRI images. In this study, we analyzed processed TBM images statistically. While morphometric images tend to provide better results for disease diagnosis compared to raw/semi-processed MRI images, they are challenging to interpret visually. Therefore, the use of DL-based methods, which enable automatic extraction of disease-specific features from difficult-to-interpret images, will address a significant research gap. The success of DL-based methods can assist physicians in diagnosing diseases using morphometric images. Hence, our goal was to contribute to AD detection by combining DL and TBM methods. Our study's results demonstrate that the proposed method, based on deep TL and TBM analysis, achieves accurate classification of three different classes and exhibits promising performance. The key highlights of our article include the following:  
* Because MRI scans are inherently three-dimensional, they can be conceptualized as a stack of 2D MRI slices. From this stack, we selected the most informative slices for classification. * MCI is a transitional stage between AD and CN. Therefore, it is difficult to diagnose. Therefore, in order to classify medical images, we employ the transfer-learning method using models trained on a large dataset. * Transfer learning is used because there is limited data available, and it helps to reduce the costs of the learning process. * Considering that morphometric images tend to yield more successful outcomes in disease diagnosis compared to raw or semi-processed MRI images, DL-based methods are employed for automatic feature extraction in the analysis of TBM images. The rest of this paper is structured as follows: Section 2 provides a summary of related studies. Section 3 presents the CNN architecture and suggested work. Section 4 discusses the results and experiments, and Section 5 presents the results of this study.