# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 2 Related Work  
AD is a prevalent neurodegenerative disorder, and its early diagnosis is of the utmost importance. Consequently, various models and techniques have been introduced by the research community to facilitate the timely identification of AD. This section presents a review of the diverse deep-learning-based approaches utilized for the identification of AD.  
Machine-learning methods, called traditional methods, were the first to appear [26; 27]. A random forest ensemble classifier with adaptive hyperparameter tuning (HPT-RFE), a novel approach that performs faster than conventional ML algorithms, was employed by [PERSON] et al. They used MRI, FDG-PET, and PIB PET data from 102 participants to make a binary classification (NC/AD: 100%; NC: 91%; AD/MCI: 95%) of data from the ADNI database [28]. By transforming the 3D sMRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database into 2D matrices and then subjecting them to a series of processing steps, [PERSON] et al. were able to accurately detect AD at the MCI stage with an SVM-based model and 96% using a deep-learning-based method. The CNN-based strategy outperformed the SVM-based method in detecting AD using sMRI data [29]. As an alternative to conventional information extraction techniques, [PERSON] et al.'s architecture, called HerstonNet, uses a 3D Resnet-based neural network regression model to extract significant characteristics from brain morphometric MRI data. By comparing HerstonNet to non-DL techniques, the consistency of the morphometric characteristics increased by 6.09% for volume, 21.73% for thickness, and 43.15% for mean curvature [30]. In a different study, [PERSON] experimented with various CNN-based pre-trained architectures to categorize 2182 images taken from the ADNI database. The EfficientNetB3 design has the best accuracy, according to the results [31]. [PERSON] et al. used MRI scans to pre-train unsupervised convolutional spiking neural networks for binary classification. They subsequently created a pipeline architecture using the SNN output as a feed for a supervised deep CNN to perform AH/MCI/NC classification tasks. They compared the classification results produced using only a CNN with those produced using this architecture. The classification performance of the SNN + CNN pipeline design was significantly better than that of the CNN-based model alone [32].  
For the multiclass classification of AD, [PERSON] et al. [33] presented deep-learning algorithms GoogLeNet, ResNet-18, and ResNet-152. Four classes (AD, LMCI, MCI, and CN) with MRI values of 33, 22, 449, and 45, respectively, were used in the experiments. ResNet-18 and ResNet-152 achieved accuracies of 98.88, 98.01, and 98.14%, respectively. [PERSON] et al. [34], using AD (198), CN 229, and MCI (408) data, used 3D CLSTM to extract deep salient features and recognized 94.19% of AD cases. Using several CNN architectures, [PERSON] et al. [35] realized fine-tuned features and reported a recognition rate of 99.05% for the diagnosis of AD. In [36], tissue segmentation was used to extract gray matter tissue from each patient. A binary class using the VGG architecture was then applied to produce a recognition rate of 98.73% for AD versus NC. Research that employs images processed for morphometric analyses is also available [37], in addition to research that uses raw or normalized MRI images as inputs to CNNs. A hybrid approach based on VBM and quantitative susceptibility mapping, an MRI technique for calculating magnetic susceptibility in tissues, was proposed by [PERSON] et al. for the early identification of AD. Their methods outperformed the conventional VBM-based method in classifying the MCI and NC groups more effectively. However, the performance of their approach's MCI/AD classification (68%) was lower than anticipated [38].